# embeddings_queue

created_at: 2026-06-01 02:52:20
topic: persistent://default/default/4951dfb5-662e-45ea-9429-9b8abc78dad1
id: 11f78688-afd9-475f-99e6-2dc09200d545
encoding: FLOAT32
metadata:
{"source":"samsung_dx_ocr","chroma:document":"삼성전자 DX부문 2026년 상반기 3급 신입사원 채용 공고\n직무\n포지선\n모집 사업부\n근무지\n모집 전공\n회로설계\nMX사업부\n수원\n회로개발\n회로설계\n미래로봇추진단\n서울\n전기전자 등 관련 전공\n시스템 개발\n네트위크사업부\n수원\n시스템 소프트웨어\n미래로봇추진단\n서울\n인공지능\nMX사업부\n수원\n인공지능\nSamsung Research\n서울\n전산 컴퓨터 전기전자, 산공 기계 통계(이공) , 수학, 물리 등 관련 전공\nS/W개발\n미들웨어 개발\n네트위크사업부\n수원\n서버 소프트웨어\n네트위크사업부\n수원\n애플리켜이선 개발\n네트위크사업부\n수원\n전산/컴퓨터 전기전자 기계, 화학 화공 재료금속 산공 통계(이공) 등 관련 전공\n품질| 서비스\n품질\nGlobal CS센터\n수원\n영상디스플레이사업부 DA사업부 MX사업부\n마켓팅\n제품서비스 마켓팅\n수원\nB2C 영업\nDA사업부 MX사업부\n수원\n해외영업\nB2B 영업\n영상디스플레이사업부 DA사업부 네트위크사업부\n수원\n전공 무관\n국내영업마켓팅\n국내영업마켓팅\n한국총관\n전국\n구매","page":1}


created_at: 2026-06-01 02:52:20
topic: persistent://default/default/4951dfb5-662e-45ea-9429-9b8abc78dad1
id: 5c07f009-f0be-49bf-8ec6-0255e9605ba6
encoding: FLOAT32
metadata:
{"source":"samsung_dx_ocr","chroma:document":"수원\n전공 무관\n국내영업마켓팅\n국내영업마켓팅\n한국총관\n전국\n구매\n구매\n네트위크사업부\n수원\n환경안전\nGlobal EHS실\n수원\n안전 등 관련 전공\n환경안전\n인프라기술\nGlobal EHS실\n구미\n기계, 건축;\n전기전자 등 관련 전공","page":1}


created_at: 2026-06-01 02:52:20
topic: persistent://default/default/4951dfb5-662e-45ea-9429-9b8abc78dad1
id: 52e5f335-0eff-4429-84a7-bb3ce52c28b9
encoding: FLOAT32
metadata:
{"page":2,"source":"samsung_dx_ocr","chroma:document":"회로개발 회로 기술을 기반으로 삼성전자 제품 및 슬루선의 학신적인 가치름 창출합니다."}


created_at: 2026-06-01 02:52:20
topic: persistent://default/default/4951dfb5-662e-45ea-9429-9b8abc78dad1
id: 92417815-7eab-4a45-9ec5-49f5a31eab30
encoding: FLOAT32
metadata:
{"page":3,"chroma:document":"MX사업부(수원 근무) 회로개발 회로설계\n포지션 소개 Job Overview\nMX사업부의 다양한 디바이스(Smartphone  Watch Buds Tablet 등의 기능과 성능 요구사항에 따라 회로-시스템올 설계 및 최적화하고 디바이스의 신회성과 완성도름 확보하는 역할울 수행합니다.\n수행업무 Job Details\n제품별 요구사항에 맞춘 디지털/RF 회로의 연구 개발 및 상품화 개발을 합니다: 고속 신호 및 전원 회로지 설계하여 효율적인 Layout올 위한 설계(Artwork)틀 합니다. 설계한 회로의 성능 및 신퇴성올 검증하고 대량 양산 대응 이수 분석올 수행합니다. 차세대 통신 단말의 RFIConnectivitylAntenna 선행 연구 개발 및 상품화 개발을 합니다: 디지털 회로와 주변 모물(오디오 카메라 등) RF 회로 간 인터페이스트 개발 및 최적화 합니다:\n자격요건 Requirements","source":"samsung_dx_ocr"}


created_at: 2026-06-01 02:52:20
topic: persistent://default/default/4951dfb5-662e-45ea-9429-9b8abc78dad1
id: ed89bd41-8bd9-4ed6-9e8d-7f4c9854cdf7
encoding: FLOAT32
metadata:
{"page":3,"source":"samsung_dx_ocr","chroma:document":"자격요건 Requirements\n전자공학 통신공학 RF 및 시스템 설계 관련 전공올 하신 분 Python/R 등 Al 활용 프로그래망 능력 및 Al 활용하 테스트 자동화 경험올 보유하신 분 RF-전자기장 신호 전달, 임피터스 매칭, 노이즈 분석에 대한 이해가 높으신 분 PCB 설계 Tool 및 다양한 시물레이선 설계 및 검증 Tool 활용 능력이 뛰어나신 분\n모바일 및 유사 전자제품 회로설계(아날로기디지털)에 대한 이해도가 높으신 분 전자회로 및 정전기(ESD), 노이즈(EMVEMC)에 대한 해석 및 디버경 경험올 보유하신 분 시물레이선 Tool(SPICE CST, HFSS) 및 CAD, Mentor 활용 능력이 뛰어나신 분 Al 기반 회로 시물레이선 데이터 분석 틀올 활용한 신호 특성 분석  회로 개선 경험올 보유하신 분 Al 모델림 및 운영올 위한 Python 등 소프트웨어 틀 사용 능력이 뛰어나신 분\n우대사항 Preferences"}


created_at: 2026-06-01 02:52:20
topic: persistent://default/default/4951dfb5-662e-45ea-9429-9b8abc78dad1
id: 9b26b9bd-236f-4ced-9e4b-860fa9f23e51
encoding: FLOAT32
metadata:
{"chroma:document":"우대사항 Preferences\n모바일 기기 개발의 핵심 엔지니어로 성장, 차세대 스마트론 웨어러블 등 주력 제품 개발에 직접 참여하고 미래 IT 분야의 리더로 도약할 수 있습니다. 입사 후, 사내 기술연구 특히 출원, 신제품 개발 경험올 쌓으면서 회로설계 전문가을 넘어 시스템 설계 품질 등 폭넓은 분야로 커리어 확장이 가능합니다 또한 Al, 시물레이선 등 소프트웨어와 하드웨어 움합올 통해 시장울 선도하는 인재로 성장할 수 있습니다:\n커리어 비전 Career Vision\n\"가장 강력한 하드웨어 학신 당신과 Galaxy가 만나면 가능합니다'\n창의적인 아이디어와 열정올 가진 인재틀 찾고 있습니다.\n도전적이고 빠르게 변화하는 환경에서 함께 성장하고 싶다면 주저하지 말고 지원해 주세요","source":"samsung_dx_ocr","page":3}


created_at: 2026-06-01 02:52:20
topic: persistent://default/default/4951dfb5-662e-45ea-9429-9b8abc78dad1
id: c794fa81-7410-4cc3-8811-5a72ab29e127
encoding: FLOAT32
metadata:
{"chroma:document":"미래로봇추진단(서울 근무) 회로개발 회로설계\n포지션 소개 Job Overview\n휴머노이드 로봇의 회로 설계름 담당하는 직무 입니다: 컴퓨팅 코어통신 인터폐이스전원 분배 보드 등에 대한 회로 설계 및 PCB Artwork올 진행하다 , 보드 안정성 확보록 통해 휴머노이드 성능의 극대화틀 목표로 합니다:\n수행업무 Job Details\n휴머노이드 하드웨어 아키택처지 설계하고 최적화 방안올 모색합니다: 컴퓨팅 코어 보드 및 통신 인터폐이스 보드 회로 설계] 합니다. PCB PVSI 시물레이선 및 실제 파형 축정올 통해 보드 안정성올 확보합니다: 전원 분배 구조 설계 및 회로 설계름 합니다: 휴머노이드 동작 중 발생하는 보드 이수 디버강올 합니다:\n자격요건 Requirements","page":4,"source":"samsung_dx_ocr"}


created_at: 2026-06-01 02:52:20
topic: persistent://default/default/4951dfb5-662e-45ea-9429-9b8abc78dad1
id: 046fc1db-b482-4cbd-99db-73f24a5a0639
encoding: FLOAT32
metadata:
{"chroma:document":"자격요건 Requirements\n전자공학  전기공학 또는 관련 전공올 하신 분 회로설계 및 PCB 디자인에 대한 기초 전공 지식올 보유하신 분 휴머노이드에 관심이 잇고 배우고 싶으신 분 기술에 대한 호기심 및 유관부서와 원활히 협업할 수 짓는 커류니키이선 태도틀 갖추신 분\n우대사항 Preferences\nCAD 틀(OrCAD, Xpedition; Altium Designer 등)올 활용한 회로 설계 경험올 보유하신 분 로봇용 회로(전원 컴퓨팅 코어/센서 등) 설계 및 적용 경험올 보유하신 분\n커리어 비전 Career Vision\n휴머노이드 하드웨어 아키택처 , 회로 설계 및 디버강 등에 대한 전반적인 기술울 습득하여 휴머노이드 분야의 전문가로 성장할 수 있습니다.\n'휴머노이드 분야 회로 개척자지 찾습니다\"\n미래 기술로 각광반는 휴머노이드 분야에서\n회로설계름 함께할 적극적이고 창의적인 아이디어름 가지신 분을 모집합니다.","source":"samsung_dx_ocr","page":4}


created_at: 2026-06-01 02:52:20
topic: persistent://default/default/4951dfb5-662e-45ea-9429-9b8abc78dad1
id: 4ca9fdc7-bba4-4551-bfc2-8fc655da4dcc
encoding: FLOAT32
metadata:
{"source":"samsung_dx_ocr","page":5,"chroma:document":"네트위크사업부(수원 근무) 회로개발 시스템개발\n포지션 소개 Job Overview\n시스템 최적화 기능 검증 성능 테스트 검증 자동화 등올 통해 통신 시스템의 성능과 품질올 보장하는 역할올 수행합니다:\n수행업무 Job Details\n통신망 설계 및 성능 개선올 위한 알고리증올 개발합니다. 통신망의 성능 분석 및 최적화지 수행합니다: 제품 기능에 대한 성능 안정성 , 호환성올 검증하고 최적화지 수행합니다.\n자격요건 Requirements\n전기 전자공학 또는 관련 전공올 하신 분 회로 설계 및 시스템 개발 기초 지식올 보유하신 분 통신 시스템 및 네트위크 도메인에 대한 이해도틀 보유하신 분 문제 해결올 위한 논리적 사고와 분석 능력올 보유하신 분\n우대사항 Preferences"}


created_at: 2026-06-01 02:52:20
topic: persistent://default/default/4951dfb5-662e-45ea-9429-9b8abc78dad1
id: 9f1e0cfa-76be-4ec6-969f-d4966fb1aebf
encoding: FLOAT32
metadata:
{"page":5,"chroma:document":"우대사항 Preferences\n통신 시스템 관련 프로적트 경험올 보유하신 분 자동화 Tool 개발(Python; Robot Framework) 혹은 SW 개발 경험흘 보유하신 분 AlML 기반 알고리롬 설계 또는 데이터 분석 경험올 보유하신 분 리뉴스 시스템에 대한 이해도틀 보유하신 분\n커리어 비전 Career Vision\n글로벌 사업자와 협력하여 국제 표준 기반 기술 개발 경험올 쌍울 수 있으펴  통신 시스템의 성능 최적화 및 검증 전문가로 성장할 수 있습니다. 차세대 네트위크 기술(5G-Advanced 6G) 개발에 참여해 최신 기술 트렌드록 선도할 수 있습니다:\n'세상을 바꾸는 최고의 네트위크릇 만들고 싶다면'\n우리가 꿈꾸는 모든 미래 기술은 끊김 없는 네트위크지 기반으로 실현-니다:\n더욱 빠르고 안정적인 네트위크로 세상올 바꾸는 여정에 함께할 여러분올 기다럽니다!","source":"samsung_dx_ocr"}


created_at: 2026-06-01 02:52:20
topic: persistent://default/default/4951dfb5-662e-45ea-9429-9b8abc78dad1
id: e94ae068-2c4d-4c24-a95e-47676ec8e342
encoding: FLOAT32
metadata:
{"source":"samsung_dx_ocr","page":6,"chroma:document":"S/W개발 소프트웨어 기술에 대한 전문적인 지식올 기반으로 창의적이고 분석적인 사고름 통해 신기술올 선도하고 당사 제품에 반영함으로써 제품 및 슬루선의 학신적인 가치름 창출합니다."}


created_at: 2026-06-01 02:52:20
topic: persistent://default/default/4951dfb5-662e-45ea-9429-9b8abc78dad1
id: ebb15e07-0509-4573-ba56-c5ad5ec157ff
encoding: FLOAT32
metadata:
{"page":7,"chroma:document":"미래로봇추진단(서울 근무) S/W개발 시스템 소프트웨어\n포지션 소개 Job Overview\n휴머노이드 로봇의 실시간 제어 시스템 및 소프트웨어 플렉품올 개발하는 직무입니다: 운영체제 환경 구성, 디바이스 드라이버, 실시간 제어 프레임위크 등 로봇 동작의 핵심 기반이 되는 소프트웨어름 설계 및 개발하다, 하드웨어 설계 조직 및 Al 연구 조직과 긴밀히 현업합니다:\n수행업무 Job Details\n휴머노이드 로봇의 실시간 제어 프레임위크 설계하고 개발합니다: 모터; 센서 등 로봇 하드웨어 제어름 위한 디바이스 드라이버 및 하드웨어 추상화 계층올 개발합니다. 로봇 제어용 운영체제(Linux 기반 실시간 OS 등) 환경올 구성하고 시스템 성능올 최적화합니다: 유관 부서와 협업하여 보행 조작 등 Al 기능과 제어 시스템 간 연동 미들웨어름 개발합니다:\n자격요건 Requirements","source":"samsung_dx_ocr"}


created_at: 2026-06-01 02:52:20
topic: persistent://default/default/4951dfb5-662e-45ea-9429-9b8abc78dad1
id: 06fc1265-76c8-4987-b165-4ce121fe01b2
encoding: FLOAT32
metadata:
{"chroma:document":"자격요건 Requirements\n컴퓨터 전기 전자, 기계 로봇공학 등 관련 전공올 하신 분 운영체제 기본 개념에 대한 이해도틀 보유하신 분 요구사항울 분석하여 소프트웨어들 구조적으로 설계 및 구현하는 역량울 보유하신 분 다양한 분야의 엔지니어와 적극적으로 소통하여 협업할 수 짓는 역량울 보유하신 분\n우대사항 Preferences\nCIC++ 기반 시스템 프로그래망 역량울 보유하신 분 Linux 기반 임베디드 시스템 개발 경험올 보유하신 분(Kernel, Device Driver, BSP 등) CAN EtherCAT, UDP 등 하드웨어 통신 프로토록 활용 경험 보유하신 분 ROS2 기반 로보텍스 프로적트 수행 경험올 보유하신 분 Git 기반 협업 및 CICD 환경에서의 개발 경험올 보유하신 분\n커리어 비전 Career Vision","source":"samsung_dx_ocr","page":7}


created_at: 2026-06-01 02:52:20
topic: persistent://default/default/4951dfb5-662e-45ea-9429-9b8abc78dad1
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{"page":7,"source":"samsung_dx_ocr","chroma:document":"커리어 비전 Career Vision\n휴머노이드 로봇의 초기 개발 단계부터 참여하여 핵심 개발자로 성장할 수 있습니다: 실시간 제어; 시스템 아키택처 , 로봇 미들웨어 등 다양한 경험올 통해 로보텍스 시스템 SW 전문가, 나아가 시스템 아키넥트로의 커리어 확장이 가능합니다.\n66 휴머노이드의 미래틀 함께 설계할 분올 찾습니다\"\n로봇이 안정적으로 동작하기 위한 소프트웨어 기반을 함께 만들어갈 도전적인 기술 과제틀 즐기는 분의 지원올 환영합니다:"}


created_at: 2026-06-01 02:52:20
topic: persistent://default/default/4951dfb5-662e-45ea-9429-9b8abc78dad1
id: c6d1b3f0-8c29-4037-bb2a-a094e4c598c9
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{"source":"samsung_dx_ocr","chroma:document":"MX사업부(수원 근무) S/W개발 인공지능\n포지션 소개 Job Overview\nMX사업부의 다양한 디바이스(Smartphone  Watch; Buds, Tablet 등) 경험흘 차별화할 Al Core 기술과 Al Agent틀 개발하여 사용자에게 새로운 경험올 제공하고 생활 전반을 변화시키는 역할올 수행합니다.\n수행업무 Job Details\n다양한 신규 Al 기술올 기반으로 Al Agent 분야의 차별화 기능올 개발합니다: MX사업부의 다양한 디바이스와 Al 서비스들올 위한 대화형 어시스템트틀 개발합니다: 차별화 핵심기술 선행 확보와 글로벌 파트너사와의 협력올 담당합니다.\n자격요건 Requirements\nAl 분야(시 학습 및 모델림, 자연어 처리 데이터 마이냥 쓸) 전공올 하신 분 안드로이드Linux 등 단말 SW, Front-end 및 Back-end 서버 개발 경험올 보유하신 분 글로벌 기업들과 영어 커유니키이선 및 현업이 가능하신 분 원활한 팀위크와 협업 능력올 보유하신 분","page":8}


created_at: 2026-06-01 02:52:20
topic: persistent://default/default/4951dfb5-662e-45ea-9429-9b8abc78dad1
id: cbe8e207-c71e-4c7a-be84-fbf293201940
encoding: FLOAT32
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{"source":"samsung_dx_ocr","page":8,"chroma:document":"우대사항 Preferences\n다양한 Al 모델 연구개발 등 직무와 연관된 대내외 활동 경험올 보유하신 분 SWIHW 플렉품올 활용한 프로제트 수행 역량올 보유하신 분 해외 법인과 커유니키이선 가능한 수준의 외국어 회화 역량울 보유하신 분 RAG Multi-Agent, SLLM 파인류빌(LORA) , 프롭프트 엔지니어령(DSPy 등)과 같은 최신 생성형 Al 기술올 실제 PoC 및 서비스 개발에 적용해본 경험올 보유하신 분\n커리어 비전 Career Vision\n직접 개발한 SW로 전세계 Galaxy 사용자에게 새로운 가치와 경험올 제공하다 최고의 SW 전문가로 성장할 수 있습니다: 입사 후 Deep Learning Machine Learning Big Data 등 다양한 SW 개발 경험올 할 수 있으며 핵심 Al 기술의 개발을 주도하는 프로적트에 참여하여 최신 기술 트랜드와 글로벌 시장 경험올 쌍울 수 있습니다.\n66 '당신의 잠재력으로 Galaxy AI의 학신올 이끌어주세요\""}


created_at: 2026-06-01 02:52:20
topic: persistent://default/default/4951dfb5-662e-45ea-9429-9b8abc78dad1
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{"chroma:document":"66 '당신의 잠재력으로 Galaxy AI의 학신올 이끌어주세요\"\n창의적인 아이디어와 열정올 가진 인재틀 찾고 있습니다.\n도전적이고 빠르게 변화하는 환경에서 함께 성장하고 싶다면 주저하지 말고 지원해 주세요","source":"samsung_dx_ocr","page":8}


created_at: 2026-06-01 02:52:20
topic: persistent://default/default/4951dfb5-662e-45ea-9429-9b8abc78dad1
id: 3b9b88a0-14d6-418f-9651-1c0cf1302409
encoding: FLOAT32
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{"chroma:document":"Samsung Research (서울 근무) S/W개발 인공지능\n포지션 소개 Job Overview\n삼성리서지에서는 DX부문의 제품 및 서비스의 사용성과 생산성 향상울 위한 인공지능 기술울 연구합니다: 인공지능 관련 전문지식과 소프트웨어 문제 해결 능력올 기반으로 선행 Al 기술울 연구 개발하여 새로운 학신올 창출하는 직무입니다:\n수행업무 Job Details","source":"samsung_dx_ocr","page":9}


created_at: 2026-06-01 02:52:20
topic: persistent://default/default/4951dfb5-662e-45ea-9429-9b8abc78dad1
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{"source":"samsung_dx_ocr","chroma:document":"수행업무 Job Details\nDX부문 제품의 사용성과 생산성 향상울 위한 생성형 Al 모델올 연구합니다. 텍스트음성 영상 등 다양한 모달리터지 연결하는 멀티리모달 Al 기술울 연구합니다. 일상과 업무에서 생성형 Al 모델의 활용성올 높이기 위한 기반 기술울 연구합니다: 자율적으로 문제 해결이 가능한 Agentic Al 기술 전반율 연구 및 개발합니다. DX부문 제품| 서비스 전반에 활용월 파운데이선 Al 모델(VLM, VLA틀 연구 및 개발합니다: 디바이스에 고성능 Al 서비스틀 탑재하기 위한 모델 최적화 및 경량화 알고리증올 연구합니다: 생성형 Al 모델올 활용하여 개인화\" 응답 서비스틀 제공하기 위한 슬루선올 연구합니다:\n자격요건 Requirements","page":9}


created_at: 2026-06-01 02:52:20
topic: persistent://default/default/4951dfb5-662e-45ea-9429-9b8abc78dad1
id: 49927a05-0f70-40e1-8ec5-0d2a93d75500
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{"page":9,"source":"samsung_dx_ocr","chroma:document":"자격요건 Requirements\n전산 컴퓨터 전기 전자, 산업공학 또는 관련 전공올 하신 분 AlML 생성형 Al, 모델 최적화 이미지/영상/ 자연어 처리 등 분야에 관심이 많으신 분 기계학습 덥러님 , 강화학습  선형대수학 , 확출/통계 등 교과목올 이수하신 분\n우대사항 Preferences\n최신 Al 분야의 논문이나 Al 모델올 구현해본 경험올 보유하신 분 Al 모델올 활용하 어플리키이선 및 서비스 개발 경험올 보유하신 분 PyTorch TensorFlow 등의 Al 프레임위크지 활용하 프로적트 경험올 보유하신 분\n커리어 비전 Career Vision\n다양하 제품 및 서비스에 시틀 응용하는 머신러님 엔지니어로 성장할 수 있습니다: Al 코어 기술부터 On-Device 적용까지 Al 관련 전반적인 기술울 연구할 수 있습니다.\n66 '삼성전자 A의 주인공이 되고 싶다면"}


created_at: 2026-06-01 02:52:20
topic: persistent://default/default/4951dfb5-662e-45ea-9429-9b8abc78dad1
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{"page":9,"source":"samsung_dx_ocr","chroma:document":"66 '삼성전자 A의 주인공이 되고 싶다면\n삼성전자 제품에 탑재되어 실질적인 도움이 되는 시틀 개발하고 연구할 수 있습니다: 플라우드부터 On-Device까지 다양한 Al 기술을 경험하고 싶으신 분들은 지원해 주세요"}


created_at: 2026-06-01 02:52:20
topic: persistent://default/default/4951dfb5-662e-45ea-9429-9b8abc78dad1
id: 3278bce9-0390-4c33-b201-ce215d5e84a8
encoding: FLOAT32
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{"source":"samsung_dx_ocr","chroma:document":"네트위크사업부(수원 근무) S/W개발 미들웨어 개발\n포지션 소개 Job Overview\n복잡한 네트위크 서비스의 뼈대름 세우고 시스템 자원올 스마트하게 통제하는 최적의 슬루선올 만드는 직무입니다\n수행업무 Job Details\n애플리키이선 개발 및 운용에 필요한 공통 라이브러리블 개발합니다: 가상화 Caas 지원 K8S API, 메시장 서비스(gRPC Protobuf 등) 어플리켜이선 간 통신 인프라틀 개발합니다: 모바일 시스템 안정적 운용올 위한 High Availability 기능 제공을 위해 이중화 서비스틀 개발합니다. AlGPU 리소스 활용올 위한 프레임위크 소프트웨어들 개발합니다:\n자격요건 Requirements\n컴퓨터 공학 전기/전자 공학 또는 관련 전공올 하신 분 C C++ 언어름 이용한 구현이 가능하신 분 문제 해결올 위한 알고리증 설계, 구현 최적화에 흥미가 있으신 분\n우대사항 Preferences","page":10}


created_at: 2026-06-01 02:52:20
topic: persistent://default/default/4951dfb5-662e-45ea-9429-9b8abc78dad1
id: bba930b5-804e-4337-be88-7e0fa973b5b7
encoding: FLOAT32
metadata:
{"page":10,"source":"samsung_dx_ocr","chroma:document":"우대사항 Preferences\n네트위크 프로그래망 개발 경험올 보유하신 분 Open Source틀 활용한 개발 경험올 보유하신 분 영어 커유니키이선 역량울 보유하신 분 Kubernetes, Cloud, Docker에l 대한 경험올 보유하신 분 국제표준(ETSI, 3GPP), 통신 Protocol에 대한 이해도틀 보유하신 분\n커리어 비전 Career Vision\n세계 최고 수준의 통신 소프트웨어 기술울 경험하다 가상화 네트위크 분야의 글로벌 기술 리더로 성장할 수 있습니다 대규모 임베디드 시스템 내 다양한 S/W틀 설계, 구현하여 통신 전문가로도 성장 가능합니다. 또한 AlGPU Framework S/W 개발을 통해 Al Framework 실무 전문가로 성장할 수 있습니다.\n'세상을 바꾸분 네트위크 속도에 기여하고 싶다면'\n우리가 꿈꾸는 모든 미래 기술은 초고속 네트위크지 기반으로 실현되니다:\n최적의 알고리증과 최고의 가상화 기술로 세상올 바꾸분 여정에 함께 할 여러분올 기다립니다!"}


created_at: 2026-06-01 02:52:20
topic: persistent://default/default/4951dfb5-662e-45ea-9429-9b8abc78dad1
id: 43f26a28-5f4e-4531-b212-665eeac32197
encoding: FLOAT32
metadata:
{"source":"samsung_dx_ocr","chroma:document":"네트위크사업부(수원 근무) S/W개발 서버 소프트웨어\n포지션 소개 Job Overview\nEnd-to-End 네트위크v 운영, 관리 최적화할 수 짓는 Al, Cloud 기반의 지능형 통합 플렉품올 개발하는 직무입니다.\n수행업무 Job Details\nAl 기반 네트위크 자동화 슬루선올 개발합니다: Al 기반 rAPP(이상 담지 에너지 절감 자원 활당 등)틀 개발합니다: Cloud Native 기반의 가상화 네트위크 운영 슬루선올 개발합니다: SDN올 개발합니다.\n자격요건 Requirements\n컴퓨터 공학, 전기/전자 공학, 수학, 통계 또는 관련 전공올 하신 분 Java Python 언어름 이용한 구현이 가능하신 분 문제 해결올 위한 알고리롬 설계 구현 최적화에 흥미가 있으신 분\n우대사항 Preferences","page":11}


created_at: 2026-06-01 02:52:20
topic: persistent://default/default/4951dfb5-662e-45ea-9429-9b8abc78dad1
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encoding: FLOAT32
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{"page":11,"source":"samsung_dx_ocr","chroma:document":"우대사항 Preferences\n국제표준(ETS1, 3GPP), 통신 Protocol에l 대한 이해도름 보유하신 분 Kubernetes Cloud, Docker에l 대한 경험올 보유하신 분 Keras; TensorFlow, Scikit-Learn 등ML Frarework 기반 개발 경험올 보유하신 분 웬 애플리베이선 개발 경험올 보유하신 분\n커리어 비전 Career Vision\n차세대 통신의 기반 기술인 가상화 및 Cloud Native 슬루선 전문가로 성장활 수 있습니다. 뿐만 아니라, 5G망을 통해 수집된 박데이터틀 기반으로 데이터 분석, Al 알고리즘 설계  자동화 슬루선 개발을 경험할 수 있습니다.\n'세상을 바꾸분 네트위크 속도에 기여하고 싶다면'\n우리가 꿈꾸는 모든 미래 기술은 초고속 네트위크지 기반으로 실현되니다:\n최적의 알고리증과 최고의 가상화 기술로 세상올 바꾸분 여정에 함께 할 여러분올 기다립니다!"}


created_at: 2026-06-01 02:52:20
topic: persistent://default/default/4951dfb5-662e-45ea-9429-9b8abc78dad1
id: c78204c6-9222-4b1d-b65e-b5ef08a09047
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{"chroma:document":"네트위크사업부(수원 근무) S/W개발 애플리키이선 개발\n포지션 소개 Job Overview\n네트위크에 필요한 다양한 기능울 SW 애플리키이선으로 구현하여 차세대 통신올 이끄는 가상화 네트위크지 만드는 역할올 수행합니다.\n수행업무 Job Details\n통신 레이어 기능별 소프트웨어들 개발합니다: 통신망 최적화틀 지원하는 리숙스 애플리키이선올 개발합니다: 초고속 통신올 위한 최적화 알고리증올 설계 개발합니다: Cloud Native 기반의 기지국 운영 관리 슬루선올 개발합니다.\n자격요건 Requirements\n컴퓨터 공학 전기/전자 공학 또는 관련 전공올 하신 분 C C++ 언어름 이용한 개발 능력올 보유하신 분 문제 해결올 위한 알고리롬 설계 구현 최적화에 흥미가 있으신 분\n우대사항 Preferences","source":"samsung_dx_ocr","page":12}


created_at: 2026-06-01 02:52:20
topic: persistent://default/default/4951dfb5-662e-45ea-9429-9b8abc78dad1
id: abea0075-6676-4e45-89b7-3e215e39f9fb
encoding: FLOAT32
metadata:
{"page":12,"chroma:document":"우대사항 Preferences\n국제표준(ETSI , 3GPP) , 통신 Protocol에l 대한 이해도틀 보유하신 분 Kubernetes, Cloud, Docker에l 대한 경험올 보유하신 분 Open Source틀 활용하 개발 경험올 보유하신 분 영어 커유니키이선 역량울 보유하신 분\n커리어 비전 Career Vision\n세계 최고 수준의 통신 소프트웨어 기술울 경험하다 가상화 네트위크 분야의 글로벌 기술 리더로 성장할 수 있습니다. 또한 대규모 임베디드 시스템 내의 다양한 소프트웨어 설계 구현 경험으로 Full Stack SW 개발자로 성장할 수 있습니다:\n'세상을 바꾸분 네트위크 속도에 기여하고 싶다면'\n우리가 꿈꾸는 모든 미래 기술은 초고속 네트위크지 기반으로 실현되니다:\n최적의 알고리증과 최고의 가상화 기술로 세상올 바꾸분 여정에 함께 할 여러분올 기다립니다!","source":"samsung_dx_ocr"}


created_at: 2026-06-01 02:52:20
topic: persistent://default/default/4951dfb5-662e-45ea-9429-9b8abc78dad1
id: dca2e674-c656-4c37-a1a5-dc526610373b
encoding: FLOAT32
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{"page":13,"source":"samsung_dx_ocr","chroma:document":"품질/서비스 신제품 개발 신회성 검종 공정 불량 검출, 고객 서비스 지원 부품 협력사 관리 등 불량 없는 제품 생산 및 고객만족 실현올 위한 슬루선올 수립하여 제공합니다."}


created_at: 2026-06-01 02:52:20
topic: persistent://default/default/4951dfb5-662e-45ea-9429-9b8abc78dad1
id: 1a2b9698-0994-4873-ad52-bce0d0a42d9f
encoding: FLOAT32
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{"source":"samsung_dx_ocr","page":14,"chroma:document":"Global CS센터(수원 근무) 품질|서비스 품질\n포지션 소개 Job Overview\n삼성전자 Global CS센터 품질팀과 기술림에서 DX부문 가전 TV모바일 등 전 제품에 대해 개발 단계별 품질올 검증하여 품질 인종올 담당하는 직무입니다. Al 및 loT, Cyber Security 등 신기능 검증과 검증 자동화 틀 개발을 담당합니다.\nDX부문 신제품에 대한 개발 단계별 고객 눈높이에 맞는 품질인증제틀 운영하다 고객이 원하는 품질이 만들어지도록 품질올 검증합니다. 하드웨어 및 소프트웨어 품질 검증 기구설계 Risk 분석, 회로 분석 및 평가 DX부문 소프트웨어 검증올 담당하다, Al 검증 온보딩 동작 자동화(Python 활용) 애플리켜이선 Hybrid(Android+Web) 개발 및 검증올 통해 소프트웨어 품질올 강화합니다. 자동화 테스트 품질 관련 서비스 등의 실행이 가능한 플빗품올 구축하여 전사의 제품공정 품질 분석 자동화틀 지원하는 역할울 수행하고 있습니다:"}


created_at: 2026-06-01 02:52:20
topic: persistent://default/default/4951dfb5-662e-45ea-9429-9b8abc78dad1
id: 7c76122c-f194-4fef-9518-8a590f18e7bd
encoding: FLOAT32
metadata:
{"source":"samsung_dx_ocr","chroma:document":"수행업무 Job Details\n자격요건 Requirements\n전기 전자 기계 소프트웨어 등 공학 관련 전공올 하신 분 기구회로 설계 및 소프트웨어 개발 관련 기초 전공 지식올 보유하신 분 DX부문 제품(가전TV모바일 등) 및 기술 트랜드에 관심이 많으신 분 유관 부서와 다양한 커류니키이선올 진행하기 위한 현업 역량울 보유하신 분\n우대사항 Preferences\n품질 직무와 연결된 대내외 활동 경험올 보유하신 분 Pythono나 SQL 등올 사용한 데이터 분석 능력 및 품질 개선 자동화 경험올 보유하신 분 Java 등 프로그래망 지식 및 플라우드 서비스 활용 경험올 보유하신 분 Al, 넥데이터 기반 테스트 설계, 신퇴성 분석, 예측 모델림 등의 경험올 보유하신 분\n커리어 비전 Career Vision","page":14}


created_at: 2026-06-01 02:52:20
topic: persistent://default/default/4951dfb5-662e-45ea-9429-9b8abc78dad1
id: 2c79f135-8de8-4f33-bb32-db199910dc05
encoding: FLOAT32
metadata:
{"source":"samsung_dx_ocr","chroma:document":"커리어 비전 Career Vision\nDX부문 품질 직속 조직에서 다양한 신제품들의 품질올 객관적으로 인증하여 고객에게 최고의 품질올 제공하는 품질 전문가로 성장할 수 있습니다: 또한 다양한 제품들의 개발 과정올 가까이서 경험하다 쌍은 개발에 대한 이해도록 바탕으로 협업 역량울 키울 수 있습니다:\n66 'DX부문 최고의 품질 전문가로 성장할 수 잇는 곳으로'\n사업부와 협업하다 고객 관점에서 품질 인증제름 운영하는 전문가로 성장할 인재틀 찾고 있습니다: 기술 트랜드에 관심이 망고 Al 및 lo7 사용 경험이 풍부하다면 주저하지 말고 지원해 주세요","page":14}


created_at: 2026-06-01 02:52:20
topic: persistent://default/default/4951dfb5-662e-45ea-9429-9b8abc78dad1
id: 40127863-1b05-452e-bc60-9a871d00de55
encoding: FLOAT32
metadata:
{"chroma:document":"마게팅 마켓 센싱 및 정보 분석 결과틀 바탕으로 당사 제품과 서비스의 차별화 가치름 소비자에제 효과적인 커유니키이선 방법으로 전달하여 목표한 경영성과루 창출하고 브랜드 가치름 제고합니다.","page":15,"source":"samsung_dx_ocr"}


created_at: 2026-06-01 02:52:20
topic: persistent://default/default/4951dfb5-662e-45ea-9429-9b8abc78dad1
id: 21c56059-45d0-4289-8acd-14002feee3b0
encoding: FLOAT32
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{"page":16,"chroma:document":"영상디스플레이사업부(수원 근무) 마켓팅 제품) 서비스 마켓팅\n포지션 소개 Job Overview\n다양한 고객 인사이트트 활용하여 제품서비스트 차별화하고 새로운 고객 경험올 발줄하다, 고객의 가치름 높이는 제품| 서비스트 기획합니다:\n수행업무 Job Details\n시장소비자/ 경쟁사/기술 분석올 통해 기회요인올 발굴하고 제품전락올 수립합니다: 소비자 라이프스타일에 맞는 제품 차별화 큰섭트틀 기획하고 신상품 큰심트틀 발굴합니다: 신규 서비스 상품 혼렌츠 기획부터 런칭 및 운영에 이르는 Life Cycle올 관리하고 Platform 경쟁력 강화름 위한 전락 파트너십올 구축합니다: 디지털 광고지면(TV Plus 등)의 글로벌 수의화 전락올 수립하다, GTM(Go-to-Market) 수행 및 캠페인 운영 등 광고 사업 전 분야름 관리합니다.\n자격요건 Requirements","source":"samsung_dx_ocr"}


created_at: 2026-06-01 02:52:20
topic: persistent://default/default/4951dfb5-662e-45ea-9429-9b8abc78dad1
id: babd064b-db46-44be-bf6d-cdcb75e6480d
encoding: FLOAT32
metadata:
{"source":"samsung_dx_ocr","chroma:document":"자격요건 Requirements\n최신 기술 동향에 대한 이해와 소비자 트렌드사용 행태에 대한 관심올 보유하신 분 데이터 기반으로 시장과 고객올 분석하는 능력올 보유하신 분 적극적으로 아이디어름 제안하고 팀위크 통해 발전시키는 협업 능력올 보유하신 분 사용자 경험 시나리오틀 설득력 잇게 스토리텔림 할 수 잇는 역량울 보유하신 분\n우대사항 Preferences\n리서치 기획 데이터 조사 및 분석 경험올 보유하신 분 제품 /서비스 프로직트 기획 경험올 보유하신 분 글로벌 커류니키이선 역량울 보유하신 분(영어 우대) OTT SNS 등 트랜드에 밝으신 분\n커리어 비전 Career Vision\n제품서비스에 대한 매출손의부터 중장기 Product Roadmap올 수립하고 학신제품 큰섭트트 발줄하는 등 제품의 Life Cycle올 구축 관리하는 전문가로 성장할 수 있습니다: 또한 컨렌츠 및 서비스분야의 전문가로서 굳렌츠 개발 운영부터 디지털 광고 OTT 파트너십까지 다양한 경험올 할 수 있습니다.","page":16}


created_at: 2026-06-01 02:52:20
topic: persistent://default/default/4951dfb5-662e-45ea-9429-9b8abc78dad1
id: 1bf6d670-3c2a-40a7-8a2b-f3efb4513699
encoding: FLOAT32
metadata:
{"page":16,"source":"samsung_dx_ocr","chroma:document":"'상상으로만 가능하던 아이디어름 현실로 만드는 기획자가 되세요'\n반짝이는 상상과 꿈꾸런 아이디어름 세상에 펼칠 수 잇는 곳 보다 나은 미래틀 만들고 함께 성장해 나갈 당신올 초대합니다!"}


created_at: 2026-06-01 02:52:20
topic: persistent://default/default/4951dfb5-662e-45ea-9429-9b8abc78dad1
id: 26a01587-d71a-40ff-9488-0b36f6226e62
encoding: FLOAT32
metadata:
{"chroma:document":"DA사업부(수원 근무) 마켓팅 제품) 서비스 마게팅\n포지션 소개 Job Overview\n고객 시장 경쟁사 기술 트랜드에 대한 이해틀 바탕으로 신규 제품올 기획하고 관리하는 직무로 고객 여정 팀구름 통해 인사이트트 찾아 새로운 제품과 서비스 영역올 발굴합니다:\n수행업무 Job Details\n기술의 발전 방향과 사회적 변화틀 예측하여 미래의 소비자가 요구하는 제품과 서비스의 큰섭트트 발굴합니다: 시장 및 소비자 트랜드틀 분석하여 제품의 차별화 기능 및 제품 포지서님 전락올 수립합니다. 판매 지역별 최적의 제품 라인업 전락울 수립하여 런칭부터 관리까지 수행합니다: 글로벌 파트너사 연계 서비스틀 기획하고 현업흘 진행합니다.\n자격요건 Requirements\n창의적인 사고와 신규 기술 제품에 대한 관심도가 높으신 분 Global 이문화에 대한 이해도가 높으신 분 팀위크와 커류니키이선 역량울 보유하신 분\n우대사항 Preferences","page":17,"source":"samsung_dx_ocr"}


created_at: 2026-06-01 02:52:20
topic: persistent://default/default/4951dfb5-662e-45ea-9429-9b8abc78dad1
id: d3d0068f-4e57-4508-b608-fdf2aefd3604
encoding: FLOAT32
metadata:
{"page":17,"chroma:document":"우대사항 Preferences\n영어로 해외 자료 조사 및 커류니키이선이 가능하신 분 직무와 연관된 대내외 활동 경험올 보유하신 분 Sprinklr Insights; Google Analytics 등올 통한 데이터 분석 능력올 보유하신 분\n커리어 비전 Career Vision\n신규 제품의 기획에서 런칭까지 주도할 기회지 제공합니다. 또한  다양한 프로적트록 통해 실무 경험올 쌍고 전문성올 높일 수 있으며 향후 관심 지역 판매 주재원 파견 및 제품 기획 전문가로 성장할 수 있습니다.\n'미래 가전 산업의 학신올 주도할 기회\"\n고객의 삶올 변화시길 새로운 가치름 창출하고\n학신적인 제품과 서비스트 함께 만들어갈 도전적이고 진취적인 인재틀 기다립니다! 제품 기획부터 시장 출시까지 전반적인 과정에 참여하는 경험올 해볼 수 있어요","source":"samsung_dx_ocr"}


created_at: 2026-06-01 02:52:20
topic: persistent://default/default/4951dfb5-662e-45ea-9429-9b8abc78dad1
id: 05992eec-3dc9-45cd-8dfc-8f576990f1c0
encoding: FLOAT32
metadata:
{"page":18,"chroma:document":"MX사업부(수원 근무) 마켓팅 제품) 서비스 마게팅\n포지션 소개 Job Overview\n디바이스(Smartphone Watch Buds Tablet 등) 경험 및 핵심 차별화지 기획/상품화하고 고객과의 효과적인 커류니키이선 전락으로 경영성과 및 브랜드가치틀 제고하는 직무입니다:\n수행업무 Job Details\n브랜드 관리 신제품 런칭 등 마켓팅 커류니키이선 전락올 수립하고 실행합니다: 온라인-디지털 마켓팅 전락올 수립하고 캠페인올 개발합니다. 런칭 이번트 전시, 홍보 영상 손렌즈틀 기획합니다: 시장 소비자 경쟁사 기술 분석올 통한 기회 요인올 발굴하고 제품 전락올 수립합니다:\n자격요건 Requirements","source":"samsung_dx_ocr"}


created_at: 2026-06-01 02:52:20
topic: persistent://default/default/4951dfb5-662e-45ea-9429-9b8abc78dad1
id: b6d67600-fbf0-4b7a-8b0b-617bbc837add
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{"page":18,"source":"samsung_dx_ocr","chroma:document":"자격요건 Requirements\n해외 법인 고객과의 원활한 커류니키이선 및 프레전테이선이 가능한 외국어 역량울 보유하신 분 다양한 채널 및 데이터틀 기반으로 시장 상황과 트랜드틀 분석할 수 짓는 역량울 보유하신 분 소비자 라이프 스타일, 제품 구매 양식 등 소비자에 대한 이해도틀 보유하신 분 최신 Tech 동향 및 Gen-Z틀 포함한 새로운 문화 Trendol 대한 이해도가 높으신 분\n우대사항 Preferences\n마켓팅/커유니키이선 관련 전공올 하신 분 또는 이에 상응하는 전공지식올 보유하신 분 Smartphone Tablet Wearable 기술에 대한 관심 및 이해도가 높으신 분\n커리어 비전 Career Vision"}


created_at: 2026-06-01 02:52:20
topic: persistent://default/default/4951dfb5-662e-45ea-9429-9b8abc78dad1
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{"page":18,"chroma:document":"커리어 비전 Career Vision\n브랜드 개선 방안 포지서님 전락 수립 및 커유니키이선 캠페인 전개 등 다양한 마켓팅 전락올 경험할 수 있으려 소비자 및 트랜드 분석올 통해 제품 기획 및 경험 기획 전문가로 성장활 수 있습니다: 또한 새로운 트랜드틀 창출하여 [T업계률 리드하는 마켓터가 월 수 있습니다.\n새로운 분야에서 도전에 목마른 당신이 필요해요'\n창의적인 아이디어와 열정올 가진 인재틀 찾고 있습니다.\n도전적이고 빠르게 변화하는 환경에서 함께 성장하고 싶다면 주저하지 말고 지원해 주세요","source":"samsung_dx_ocr"}


created_at: 2026-06-01 02:52:20
topic: persistent://default/default/4951dfb5-662e-45ea-9429-9b8abc78dad1
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encoding: FLOAT32
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{"chroma:document":"해외영업 고객과 시장 제품에 대한 이해틀 바탕으로 시장 수요와 경쟁환경올 분석하여 국가/ 거래선별 목표 설정 영업전락 수립 신규 제품, 영업 채널올 발꿀하고 판매전락 수립 및 실행올 통해 매출 극대화에 기여합니다.","page":19,"source":"samsung_dx_ocr"}


created_at: 2026-06-01 02:52:20
topic: persistent://default/default/4951dfb5-662e-45ea-9429-9b8abc78dad1
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{"chroma:document":"DA사업부(수원 근무) 해외영업 B2C 영업\n포지션 소개 Job Overview\n글로벌 시장과 소비자에 대한 이해틀 바탕으로 생활가전 제품의 판매 전락올 수립하고 매출 확대와 브랜드 리더십올 제고합니다.\n수행업무 Job Details\n담당 지역의 시장과 고객 특성, 판매 데이터들 분석하여 매출과 손의 극대화틀 위한 제품 가격유통 마켓팅올 아우르는 장단기 판매 전락올 수립합니다. 수립든 판매 전락올 기반으로 담당 법인과 현업하여 적기 공급올 위한 오퍼레이선올 지원하고 현지 법인의 실행 계획과 진척올 관리 및 개선합니다.\n자격요건 Requirements\n영어로 해외 자료 조사 및 커류니키이선이 가능하신 분 Global 이문화에 대한 이해도가 높으신 분 팀위크와 협업 능력올 보유하신 분\n우대사항 Preferences\n직무와 연관된 대내외 활동 경험올 보유하신 분 제2외국어 회화 역량울 보유하신 분\n커리어 비전 Career Vision","source":"samsung_dx_ocr","page":20}


created_at: 2026-06-01 02:52:20
topic: persistent://default/default/4951dfb5-662e-45ea-9429-9b8abc78dad1
id: b50dcb64-cea7-4859-8f89-b545f229e701
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{"page":20,"source":"samsung_dx_ocr","chroma:document":"커리어 비전 Career Vision\n담당 지역의 언어와 문화 습득올 포함한 글로벌 역량울 강화할 수 있으려 고객시장 유통 특성 등 전문 지식 습득과 법인 관리 및 개선올 통한 영업 실무 경험올 쌍울 수 있습니다. 삼성전자 해외영업 주재원으로서 현지 법인에서 근무하여, 매출 확대와 브랜드 리더십올 공고히 하는 현장 경험올 익히려 글로벌 영업 전문가로 성장할 수 있습니다:\n'글로벌올 무대로 DA의 새로운 가능성울 열어갈 당신에게\"\n시장과 소비자에 대한 이해틀 바탕으로\n생활가전 브랜드틀 최고의 자리로 이끌어 칼 인재틀 모십니다."}


created_at: 2026-06-01 02:52:20
topic: persistent://default/default/4951dfb5-662e-45ea-9429-9b8abc78dad1
id: 74b55631-4b9a-4f25-b94b-7a29296d8def
encoding: FLOAT32
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{"source":"samsung_dx_ocr","page":21,"chroma:document":"MX사업부(수원 근무) 해외영업 B2C 영업\n포지션 소개 Job Overview\nMX사업부의 고객과 시장, 제품에 대한 이해틀 바탕으로 시장 수요와 경쟁 환경올 분석하여 국가/거래선(사업자 대리점)별 목표 설정 및 영업전락올 수립하고 신규 제품|영업채널 발굴, 판매전락 수립 및 실행올 통해 매출 극대화에 기여하는 직무입니다:\n수행업무 Job Details\n채널별 트랜드 분석, 거래선별 프로모선올 현의하고 온라인 전락올 수립합니다. SCM 기반 제품 공급, 유통 재고 관리, 매출 손의 시장 점유울 목표 대비 진척도틀 관리합니다. 경쟁사 분석올 통한 가격 포지서님 전락올 수립, 신제품올 제안 개발 일정올 관리합니다: 시장수요 및 당사 경쟁사 데이터 분석에 기반하여 영업 목표 및 전락올 수립합니다.\n자격요건 Requirements"}


created_at: 2026-06-01 02:52:20
topic: persistent://default/default/4951dfb5-662e-45ea-9429-9b8abc78dad1
id: 5838c59f-eecb-4429-9d7a-0e3b7746b7d4
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{"source":"samsung_dx_ocr","chroma:document":"자격요건 Requirements\n데이터지 분석하고 이틀 바탕으로 의미 짓는 결과루 도출하는 역량울 보유하신 분 해외 법인 및 거래선과 원활한 커유니키이선이 가능한 수준의 외국어 회화 역량울 보유하신 분 이머징 시장에 진취적이고 도전적이다 Global 이문화에 대한 이해도가 높으신 분\n우대사항 Preferences\n시장 상황 및 고객 데이터지 정확하게 분석하고 이틀 바탕으로 전락올 수립한 경험이나 해외영업 관련 대외활동올 경험하신 분 제2외국어 회화 역량울 보유하신 분 전자제품 트랜드에 민감하고 MX사업부 제품 및 기술에 대한 이해도가 높으신 분\n커리어 비전 Career Vision\n다양한 현장 경험올 통해 모바일 시장울 이끄는 영업 전문가로 성장할 수 있으려, 전세계 대규모 거래선과 협력하여 글로벌 커유니키이선 능력올 키울 수 있습니다: 또한 제품의 기획 및 생산 단계부터 소비자 판매 시점까지의 전 과정에 참여함으로써 비즈니스 운영 역량울 확보할 수 있습니다:","page":21}


created_at: 2026-06-01 02:52:20
topic: persistent://default/default/4951dfb5-662e-45ea-9429-9b8abc78dad1
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{"page":21,"source":"samsung_dx_ocr","chroma:document":"'전세계 스마트혼 시장울 선도하여 글로벌 비즈니스트 주도할 전문가로 도약하고 싶다면\n창의적인 아이디어와 열정올 가진 인재틀 찾고 있습니다.\n도전적이고 빠르게 변화하는 환경에서 함께 성장하고 싶다면 주저하지 말고 지원해 주세요"}


created_at: 2026-06-01 02:52:20
topic: persistent://default/default/4951dfb5-662e-45ea-9429-9b8abc78dad1
id: f6eb41f4-c2bb-4282-8add-0ac906e897ed
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{"chroma:document":"영상디스플레이사업부(수원 근무) 해외영업 B2B 영업\n포지션 소개 Job Overview\n시장트랜드 센싱올 바탕으로 사업 전락올 분석 및 수립하고 다양한 영업활동올 통해 해외 업체들과의 파트너십올 구취관리하여 Tizen OS 라이센싱 및 아웃소상 사업흘 확장하고 새로운 사업기회틀 발굴합니다.\n수행업무 Job Details\n시장기술경쟁사 협력업체 환경 변화 분석과 데이터 기반 사업 분석올 담당합니다: Tizen OS 라이센싱 사업 확장올 위해 파트너십올 관리하고 신규 기회블 발굴합니다: 트랜드 센상올 바탕으로 자가/JDM라이센싱 모델 운영전락올 수립합니다.\n자격요건 Requirements\n영어 능통자로 해외고객과 원활한 커류니키이선 능력올 보유하신 분 데이터 기반 시장/산업 분석 능력올 보유하신 분 다양한 과제틀 처리할 수 있는 유연성과 문제 해결 능력올 보유하신 분\n우대사항 Preferences","page":22,"source":"samsung_dx_ocr"}


created_at: 2026-06-01 02:52:20
topic: persistent://default/default/4951dfb5-662e-45ea-9429-9b8abc78dad1
id: 57344939-7928-42d9-8246-842a8a667957
encoding: FLOAT32
metadata:
{"chroma:document":"우대사항 Preferences\n'Consumer Electronics' 및 'IT Service' 산업에 대한 이해도가 높으신 분 해외영업 직무와 연관된 대내외 활동 경험올 보유하신 분 제2외국어 회화 역량울 보유하신 분(특수어 전공자 등) 시장 제품/경쟁사 데이터 분석 통한 영업 Insight 발굴 및 전락 수립 능력올 보유하신 분\n커리어 비전 Career Vision\n국내외 시장과 B2B 고객에 대한 이해틀 바탕으로 학신적 사업 모델올 제안하는 차세대 영업 리더로 성장활 수 있습니다: 또한  출장 , 파견 등 다양한 현장 경영 경험올 통해 글로벌 비즈니스 역량 확보가 가능합나다:\n\"TV OS 라이센싱 JDM 등 다양한 B2B 사업 성장울 경험하고 싶다면\n활발하고 적극적인 자세로 과제에 임할 수 잇는 인재틀 찾고 있습니다:\n다양한 경험과 비즈니스 역량울 키울 수 잇는 기회지 잡고 싶다면 주저하지 말고 지원해 주세요","source":"samsung_dx_ocr","page":22}


created_at: 2026-06-01 02:52:20
topic: persistent://default/default/4951dfb5-662e-45ea-9429-9b8abc78dad1
id: 599920dc-4d8b-4b5d-8d43-4daa182a8d1c
encoding: FLOAT32
metadata:
{"chroma:document":"DA사업부(수원 근무) 해외영업 B2B 영업\n포지션 소개 Job Overview\n글로벌 시장과 파트너사에 대한 이해틀 바탕으로 생활가전 제품의 B2B(Business-to-Business) 판매 전락올 수립, 매출 확대와 브랜드 리더십올 제고합니다.\n수행업무 Job Details\n담당 지역의 시장과 파트너사의 특성 및 업계 데이터지 분석하여 당사 매출과 손의 극대화틀 위한 B2B 특화 장단기 판매 전락올 수립합니다: 수립든 판매 전락올 기반으로 담당 법인과 현업하여 적기 공급올 위한 오퍼레이선올 지원하고 현지 법인의 실행 계획과 진척올 관리 및 개선합니다. B2B 연계 신사업 영역 개척 및 신규 파트너사 확대 방안올 수립하고 추진합니다:\n자격요건 Requirements\n영어로 해외 자료 조사 및 커류니키이선이 가능하신 분 Global 이문화에 대한 이해도가 높으신 분 적극적이고 진취적인 태도로 신규 영역의 개척 의지가 강하신 분 팀위크와 협업 능력올 보유하신 분","source":"samsung_dx_ocr","page":23}


created_at: 2026-06-01 02:52:20
topic: persistent://default/default/4951dfb5-662e-45ea-9429-9b8abc78dad1
id: 3ee4d374-cd85-43dc-b6ba-ddd15e91eb22
encoding: FLOAT32
metadata:
{"page":23,"source":"samsung_dx_ocr","chroma:document":"우대사항 Preferences\n직무와 연관든 대내외 활동 경험올 보유하신 분 제2외국어 회화 역량울 보유하신 분\n커리어 비전 Career Vision\n가전 업계 B2B 전문 지식 습득과 법인 관리 및 개선올 통한 영업 실무 경험올 쌍울 수 있습니다: 삼성전자 해외영업 주재원으로서 현지 법인에서 근무하다 매출 확대와 브랜드 리더십울 공고히 하는 현장 경험올 익히려 글로벌 영업 전문가로서 성장할 수 있습니다:\nB2B 영업 리더로 성장하고 싶다면\"\n창의적 사고와 전락적 마인드 실행력올 갖춘 열정적인 인재틀 찾고 있습니다:\n학신적인 제품과 기술로 글로벌 무대에서 활약하고 싶다면 주저하지 말고 지원해 주세요"}


created_at: 2026-06-01 02:52:20
topic: persistent://default/default/4951dfb5-662e-45ea-9429-9b8abc78dad1
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{"page":24,"source":"samsung_dx_ocr","chroma:document":"네트위크사업부(수원 근무) 해외영업 B2B 영업\n포지션 소개 Job Overview\n글로벌 통신 시장올 분석해 고객별 맞춤 슬루선올 발굴하고 최적화된 영업 전락과 판매 목표릎 수립하여 매출 및 이익 극대화에 기여합니다:\n수행업무 Job Details\n시장 경쟁사틀 분석해 신사업 기회블 모색합니다. 고객의 요구사항 파악 전락적 현상 계약 체결 업무름 수행합니다: 기술과 제품올 기반으로 지역/고객별 맞춤형 제안 전락올 수립합니다. 기술 트랜드틀 반영한 기술 슬루선 제안 및 수주계약올 관리합니다 글로벌 마켓팅 전락올 수립 및 실행합니다.\n자격요건 Requirements\n해외 법인고객사와 소통하기 위한 외국어 회화 역량울 보유하신 분 글로벌 문화에 대한 이해와 열런 자세름 보유하신 분 신규 시장 발줄올 위한 도전 의식올 보유하신 분\n우대사항 Preferences"}


created_at: 2026-06-01 02:52:20
topic: persistent://default/default/4951dfb5-662e-45ea-9429-9b8abc78dad1
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{"page":24,"chroma:document":"우대사항 Preferences\n통신 시장에 대한 이해와 관심올 보유하신 분 시장트랜드에 대한 분석 능력올 보유하신 분(시장 동향 파악 데이터 분석 등) 전기 전자공학 또는 관련 전공올 하신 분 직무 관련 대내외 활동 경험올 보유하신 분 제2외국어 회화 역량울 보유하신 분\n커리어 비전 Career Vision\n글로벌 시장 현장 경험올 통해 사업 확대들 리드하는 B2B 영업 전문가로 성장활 수 있습니다. B2B 사업의 모든 과정에 참여하다 사업 운영 역량울 키우고(사업 수주계약 장비 공급 설치, 서비스등) 제품 기술 및 다양한 B2B 고객에 대한 이해틀 통해 학신적 사업 모델올 제안하는 차세대 영업 리더로도 성장할 수 있습니다.\n'B2B 영업의 모든 것' 알고 싶다면\n전세계 주요 통신 사업자와 함께 국가의 지도록 바굽니다:\n기술력과 비즈니스 인사이트트 더해 삼성전자 네트위크 영토름 넓혀갈 전락가을 찾습니다!","source":"samsung_dx_ocr"}


created_at: 2026-06-01 02:52:20
topic: persistent://default/default/4951dfb5-662e-45ea-9429-9b8abc78dad1
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created_at: 2026-06-01 02:52:20
topic: persistent://default/default/4951dfb5-662e-45ea-9429-9b8abc78dad1
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{"chroma:document":"한국종팔(전국 근무) 국내영업마켓팅\n포지션 소개 Job Overview\n삼성전자 DX부문에서 국내 시장에 출시하는 전 제품과 서비스의 국내 영업 마켓팅올 총관하는 직무입니다. 온 오프라인 유통 채널별 영업 전락 및 제품과 브랜드의 마켓팅 전락올 수립하다 경영 성과름 창출하고 있습니다.\nB2CB2B온라인 경로의 판매 전락올 수립하고 신규 거래선과 Biz 틀 발출합니다: 제품의 품목별 매출 손의올 관리하고 소비자 분석올 통한 가격 라인업 프로모선 등 효과적인 마켓팅 전락올 수립합니다: 국내시장 판매 목표름 설정하고 공급망올 관리(SCM)하여 담당 품목의 프트플리오 전반올 총팔합니다. 소비자에게 제품 및 브랜드 가치 전달하기 위한 광고캠페인 등 (MC전락올 수립합니다. 오프라인 매장의 리타일 마켓팅 전락올 수립합니다. 판매 직원 교육 관리 현장 Data 분석 등 소비자 접점의 판매 경쟁력올 높입니다.\n수행업무 Job Details\n자격요건 Requirements","page":26,"source":"samsung_dx_ocr"}


created_at: 2026-06-01 02:52:20
topic: persistent://default/default/4951dfb5-662e-45ea-9429-9b8abc78dad1
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{"page":26,"chroma:document":"수행업무 Job Details\n자격요건 Requirements\n전자 제품 및 기술 트랜드에 민감하고 창의력과 도전정신올 보유하신 분 시장유통고객 트랜드에 대한 이해도가 높고 시장 센싱 및 분석 역량울 보유하신 분 거래선 및 유관 부서와 원활한 관계름 멋올 수 짓는 커유니키이선 능력올 보유하신 분\n우대사항 Preferences\n경영 기초 마켓팅 , 통계 관련 전공올 하신 분 또는 이에 상응하는 전공지식올 보유하신 분 영업 및 마켓팅 직무와 연관된 대내외 활동 경험올 보유하신 분 (공모전, 인터, 사업 경험 또는 그에 준하능 경험)\n커리어 비전 Career Vision\n신모델 런칭부터 공급망 관리까지, PLC(Product Life Cycle의 전 프로세스트 관리하는 차별화된 커리어 경험올 쌍울 수 있는 곳입니다. 또한  국내 영업 마켓팅 전문성올 기반으로 글로벌 양성 기회블 통해 해외시장울 선도하는 영업마켓팅 전문가로 성장할 수 있습니다:","source":"samsung_dx_ocr"}


created_at: 2026-06-01 02:52:20
topic: persistent://default/default/4951dfb5-662e-45ea-9429-9b8abc78dad1
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{"source":"samsung_dx_ocr","chroma:document":"'국내시장 최고의 영업 마켓팅 조직, 한국총관\n창의적이고 도전적인 영업 마켓팅 인재틀 찾습니다:\n열정과 도전정신올 갖춘 동료들과 함께 성장하고 싶다면 주저하지 말고 지원해 주세요","page":26}


created_at: 2026-06-01 02:52:20
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created_at: 2026-06-01 02:52:20
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{"source":"samsung_dx_ocr","page":28,"chroma:document":"네트위크사업부(수원 근무) 구매\n포지션 소개 Job Overview\n글로벌 SCM 데이터지 기반으로 구매 프로세스트 학신하고 최적의 파트너십올 통해 사업의 이익과 경쟁력올 극대화합니다.\n수행업무 Job Details\n부품별 구매 Lead Time올 효율적으로 관리해 제품의 적시 생산과 공급올 지원하고 원자재 적정 재고 운영으로 사업부 실적올 극대화합니다 부품의 핵심 원자재와 생산 공정올 분석하여 공급 Lead Time 및 가용량울 최적화합니다. 제품의 생산/판매 계획 및 중장기 물동 변화에 긴밀하게 대응하다, 이와 연계하여 부품 Forecast틀 관리합니다:\n자격요건 Requirements\n해외 협력사와의 소통올 위한 글로벌 커류니키이선 능력올 보유하신 분(영어 및 제2외국어) 최신 네트위크 기술 동향 파악, 비즈니스 현상 전락 공급망 관리(SCM)에 대한 높은 이해도와 관심올 보유하신 분 내부 팀위크 및 협력사 간 원활한 현업으로 목표름 달성하는 협력적 태도와 실행력올 보유하신 분"}


created_at: 2026-06-01 02:52:20
topic: persistent://default/default/4951dfb5-662e-45ea-9429-9b8abc78dad1
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{"page":28,"source":"samsung_dx_ocr","chroma:document":"우대사항 Preferences\n구매 업무 또는 공급망 관리(SCM) 경험올 보유하신 분 재고 관리 물류 프로세스에 대한 기본 원리와 실무 지식올 보유하신 분 제조생산 공정 전반에 대한 높은 이해도틀 보유하신 분\n커리어 비전 Career Vision\n조달 재괴물류 등 공급망 관리 전반에 결친 실무 경험올 바탕으로 SCM 전문가로 성장할 수 있습니다: 또한 해외 생산법인과의 업무 협업 소통올 통해 해외 구매 주재원으로 성장활 수 있으다 다양한 공급망 리스크 대응 경험올 바탕으로 한 전락적 사고와 역량울 키위 사업부 및 전사 공급망 리더로 성장할 수 있습니다.\n'탁월한 가격 현상 전락으로 기업의 이익에 적극 기여하고 싶다면=\n기지국과 네트위크 슬루선올 구성하는 부품의 공급망 리스크틀 줄이고 제품 경쟁력올 제고합니다. 글로벌 공급망을 무대로 성장해나갈 여러분의 도전올 기다림니다!"}


created_at: 2026-06-01 02:52:20
topic: persistent://default/default/4951dfb5-662e-45ea-9429-9b8abc78dad1
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created_at: 2026-06-01 02:52:20
topic: persistent://default/default/4951dfb5-662e-45ea-9429-9b8abc78dad1
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{"source":"samsung_dx_ocr","page":30,"chroma:document":"Global EHS실(수원 근무) 환경안전 환경안전\n포지션 소개 Job Overview\n안전 관련 법과 규정올 준수하다 사내 위험요소의 원인올 분석해 제거하고 임직원 대상 홍보 및 교육올 통해 안전하고 건강하게 일할 수 짓는 회사틀 만드는데 기여하는 직무입니다.\n수행업무 Job Details\n전사 안전 분야 표준 제-개정 및 업무 표준화루 수립합니다. 안전보건 Audit 및 Compliance 대응 체계틀 구축하고 운영합니다. 잠재 위험 발굴 및 위험성 평가 관리 안전기준 수립 산업재해조사 재발방지 대책 수립 및 이행올 합니다: 안전 관련 법정교육 기획 운영 관리 및 임직원 안전의식 고취 활동올 합니다.\n자격요건 Requirements\n안전공학 또는 관련 전공올 하신 분 기본과 원칙올 중시하고 분석적인 사고력 논리력올 바탕으로 문제해결 역량울 보유하신 분 팀위크와 현업 능력올 보유하신 분\n우대사항 Preferences"}


created_at: 2026-06-01 02:52:20
topic: persistent://default/default/4951dfb5-662e-45ea-9429-9b8abc78dad1
id: bd85e0ee-c2b2-4fdd-8a37-dab2e8f9d896
encoding: FLOAT32
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{"chroma:document":"우대사항 Preferences\n안전 직무 관련 국가기술자격올 보유하신 분(기사 이상) 안전 직무 관련 대내외 경험올 보유하신 분(프로적트 논문; 특히 경진대회 등) 해외법인과 커유니키이선이 가능한 수준의 외국어(영어 중국어) 회화 역량울 보유하신 분\n커리어 비전 Career Vision\n안전 분야 법구름 반영한 사내 기준 수립 관리블 통해한 사고 예방 및 잠재 리스크트 개선하는 안전 전문가로 성장합니다:\n66 '안전올 디자인하다\"\n함께 성장하다 지속 가능한 안전 문화틀 만들 인재름 창고 있습니다. 전문성과 책임감올 가진 안전 인재틀 기다립니다!","source":"samsung_dx_ocr","page":30}


created_at: 2026-06-01 02:52:20
topic: persistent://default/default/4951dfb5-662e-45ea-9429-9b8abc78dad1
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{"page":31,"chroma:document":"Global EHS실(구미 근무) 환경안전 인프라기술\n포지션 소개 Job Overview\n사업장 전력/에너지 공급 및 공조등 Utility틀 안전하고 안정적으로 공급하기 위하여 시스템 기획/설계, 유지 보수 운영기준 등올 수행하는 직무입니다:\n수행업무 Job Details\n중앙 공조설비 및 환경 설비 운영 법적 설비 관리플 합니다. 설비용량 산출 및 신규 설비 도입 에너지 효율 개선 검토틀 합니다: 공급 품질 관리, 부하울 관리, 불합리 발걸/조치 등 개선 활동올 통해 생산 공정에 필요한 Facility/Utility틀 최적 조건하 운영합니다: 운영기준 표준 제개정올 통해 프로세스틀 관리하고 환경영항평가루 통해 안전사고름 예방합니다:\n자격요건 Requirements\n기계, 건축 전기전자 또는 관련 전공올 하신 분 기본과 원칙올 중시하고 분석적인 사고력논리력올 바탕으로 문제해결 역량울 보유하신 분 팀위크와 협업 능력올 보유하신 분\n우대사항 Preferences","source":"samsung_dx_ocr"}


created_at: 2026-06-01 02:52:20
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{"chroma:document":"우대사항 Preferences\n설비 직무 관련 국가기술자격올 보유하신 분(기사 이상) 직무 관련 대내외 경험흘 보유하신 분(프로적트 논문 특히, 경진대회 등) 해외법인과 커유니키이선이 가능한 수준의 외국어(영어, 중국어) 회화 역량울 보유하신 분\n커리어 비전 Career Vision\n인프라 분야의 전문역량울 통한 사고 예방 및 잠재 리스크트 개선하는 환경안전 전문가로 성장합니다.\n66 '인프라틀 지팅하다\"\n안정적인 시스템 운영으로 조직의 기반을 함께 만들어칼 인재틀 찾고 있습니다. 책임감과 실행력올 갖춘 인프라 운영 인재틀 기다럽니다!","page":31,"source":"samsung_dx_ocr"}


created_at: 2026-06-01 04:12:39
topic: persistent://default/default/80d8d54a-809f-4b2e-89e9-7bb042a24d9e
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created_at: 2026-06-01 04:13:05
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created_at: 2026-06-01 04:13:05
topic: persistent://default/default/80d8d54a-809f-4b2e-89e9-7bb042a24d9e
id: 7beecfe0-8f27-4194-a87a-aaff7bf267b2
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{"creationdate":"2026-03-12T11:22:44+09:00","producer":"Adobe PDF Library 18.0","page":1,"moddate":"2026-03-12T11:30:50+09:00","trapped":"/False","creator":"Adobe InDesign 21.0 (Windows)","source":"./data/제주관광가이드.pdf","page_label":"2","total_pages":42,"chroma:document":"저옵서\nPOCKET BOOK\n제주야, 이제 내 고향하자!\n고향사랑 기부제는 개인이 고향에 기부하면 세액공제와\n답례품(지역특산품)을 받는 제도입니다"}


created_at: 2026-06-01 04:13:05
topic: persistent://default/default/80d8d54a-809f-4b2e-89e9-7bb042a24d9e
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created_at: 2026-06-01 04:13:05
topic: persistent://default/default/80d8d54a-809f-4b2e-89e9-7bb042a24d9e
id: 87af26d2-21b5-431d-a45f-2b973e871e00
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{"page_label":"4","producer":"Adobe PDF Library 18.0","page":3,"total_pages":42,"creator":"Adobe InDesign 21.0 (Windows)","creationdate":"2026-03-12T11:22:44+09:00","moddate":"2026-03-12T11:30:50+09:00","source":"./data/제주관광가이드.pdf","chroma:document":"About Jeju\n제주도 알아보기 06\n제주의 날씨와 환경 10\n주요도로소개 12\n대중교통안내 13\n렌터카   〮   14\n버스   〮   16\n택시   〮   17\n관광지 순환버스   〮   18\n제주시티투어버스   〮   20\nCulture Jeju\n생활 속 제주어 22\n제주에서 꼭 맛봐야 할 음식 24\n향토음식   〮   24\n일반음식   〮   28\n제주도 숙소 소개 30\nTravel Jeju\n제주관광 추천 30 32\n제주의 계절 관광지 40\n봄   〮   40\n여름   〮   42\n가을   〮   44\n겨울   〮   46\n다크 투어리즘 48\n제주 여행 버킷리스트 50\n한라산 트레킹   〮   50\n올레길 탐방   〮   52\n레져 스포츠 여행   〮   54\n자전거 여행   〮   55\n거문오름 탐방   〮   56\n사려니숲길   〮   57\n제주 여행 쇼핑리스트 58\n추천여행코스 60\nInformation\n제주의 축제 68\nMICE관광 70\n주요전화번호 72","trapped":"/False"}


created_at: 2026-06-01 04:13:05
topic: persistent://default/default/80d8d54a-809f-4b2e-89e9-7bb042a24d9e
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{"source":"./data/제주관광가이드.pdf","chroma:document":"Information\n제주의 축제 68\nMICE관광 70\n주요전화번호 72\nCONTENTS CONTENTS","moddate":"2026-03-12T11:30:50+09:00","total_pages":42,"page_label":"4","producer":"Adobe PDF Library 18.0","trapped":"/False","page":3,"creationdate":"2026-03-12T11:22:44+09:00","creator":"Adobe InDesign 21.0 (Windows)"}


created_at: 2026-06-01 04:13:05
topic: persistent://default/default/80d8d54a-809f-4b2e-89e9-7bb042a24d9e
id: ff42a82d-a2a3-496f-aa5e-b26850417ed3
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{"producer":"Adobe PDF Library 18.0","creator":"Adobe InDesign 21.0 (Windows)","page_label":"5","total_pages":42,"creationdate":"2026-03-12T11:22:44+09:00","page":4,"moddate":"2026-03-12T11:30:50+09:00","source":"./data/제주관광가이드.pdf","trapped":"/False","chroma:document":"추자도\n비양도\n가파도\n마라도\n우도\n서귀포시\n제주시\n한라산\n위치 동경 126도 08분 ~ 126도 58분  /  북위 33도 06분 ~ 34도 00분  \n날씨 강수량(㎜) 제주 1,728.3 서귀포 2,618.1  /  평균기온(℃) 16.7  \n면적 총면적 1,849.1㎢  /  최고점 한라산 1,950m\n상징하는 꽃 참꽃  상징하는 나무 녹나무 상징하는 새 제주큰오색딱따구리  상징하는 색 파랑\n삼다도 돌, 바람, 여자가  많아서 삼다도라 불리는 섬.\n삼무도 도둑, 거지, 대문이 없어서 삼무도라고도 불리는 섬. \n탐라 신비로운 탐라 천년의 역사를 간직한 섬.\n제주도 알아보기\n우리나라에서 가장 큰 섬이며 특별자치도이다.\n대한민국 최남단에 위치한 불과 물이 빚어낸 세계적인 화산섬.\n대륙(러시아, 중국)과 해양(일본, 동남아)을 연결하는 요충지. \n천혜의 자연경관이 수려한 세계적인 휴양섬."}


created_at: 2026-06-01 04:13:05
topic: persistent://default/default/80d8d54a-809f-4b2e-89e9-7bb042a24d9e
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{"page_label":"5","chroma:document":"천혜의 자연경관이 수려한 세계적인 휴양섬.\n동서 길이 73km, 남북 길이 31km의 타원형 섬. 일주도로 길이 176km, 해안선 551.78km.\n겨울에도 영하로 떨어지는 법이 거의 없는 온대 기후. 오름의 왕국이자 나비와 같은 곤충들의 천국.\n한라산을 중심으로 아열대, 온대, 한대 식물이 공존하는 식물의 보고.\n기본 정보\n아름다운 숲과 화려한 도시. 그리고 푸른 바다까지 즐기고 싶은 여행객이라면 제주도는 탁월한 \n선택이 될 것이다. 유네스코 자연유산에 등재된 천혜의 섬 제주도는 볼거리, 먹을거리, 즐길 거리가 \n가득한 섬으로 매년 한국인이 가장 방문하고 싶은 관광지 1위의 자리를 고수하고 있는 곳이다. \n06JEJU   ISL AND   POCKET   BOOK —07\nAbout  Jeju","source":"./data/제주관광가이드.pdf","total_pages":42,"moddate":"2026-03-12T11:30:50+09:00","creator":"Adobe InDesign 21.0 (Windows)","page":4,"trapped":"/False","producer":"Adobe PDF Library 18.0","creationdate":"2026-03-12T11:22:44+09:00"}


created_at: 2026-06-01 04:13:05
topic: persistent://default/default/80d8d54a-809f-4b2e-89e9-7bb042a24d9e
id: 888b2dba-37d7-4d84-be2e-5316ee61f02a
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{"total_pages":42,"page":5,"producer":"Adobe PDF Library 18.0","moddate":"2026-03-12T11:30:50+09:00","creator":"Adobe InDesign 21.0 (Windows)","page_label":"6","trapped":"/False","source":"./data/제주관광가이드.pdf","chroma:document":"보물섬 제주도에 가면 꼭 봐야 할 곳, 꼭 맛봐야 할 것들은?\n제주 7대 명품은 관광과 1차 산업, 3차 산업 등을 연계한 제주 비경과 특산물(1차산업 생산물), \n향토음식, 아름다운 도로, 아름다운 건축물 5개 분야로 나눠져 있다. 각 분야별로 설문조사와 \n학계 및 전문가의 추천 심의 등을 거쳐 선정한 명품들로 ‘보물섬’ 제주도를 찾는 사람이라면 꼭 \n경험해야할 42가지의 특별함으로 기억될 것이다.\n제주7대 비경\n제주도의 겨울은 타 지역에 비해 아주 포근한 편이지만 바람이 거세다. 바닷가 쪽으로 나가면 \n바람이 등 떠미는 게 느껴지지만 에메랄드 빛 바다와 우뚝 솟은 한라산을 보고 있노라면 \n감탄사가 절로 나온다.\n━\n푸른바다 위의 일출봉과 우도 | 감귤밭에서 바라보는 한라산 | 거문오름에서 바라보는 분화구 | \n서귀포 칠십리공원에서 보는 천지연폭포 | 섭지코지 촛대바위 일출 | 지삿개 주상절리와 중문색달\n해수욕장 절경 | 협재에서의 저녁노을\n제주7대 향토음식","creationdate":"2026-03-12T11:22:44+09:00"}


created_at: 2026-06-01 04:13:05
topic: persistent://default/default/80d8d54a-809f-4b2e-89e9-7bb042a24d9e
id: ed207f2f-b1ea-4489-9ffe-04ed2f06d49d
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{"producer":"Adobe PDF Library 18.0","moddate":"2026-03-12T11:30:50+09:00","chroma:document":"해수욕장 절경 | 협재에서의 저녁노을\n제주7대 향토음식\n제주도의 자연과 사회 문화적 환경을 배경\n으로 형성된 제주의 음식 문화는 독특하다. \n지리적으로 사면이 바다여서 사시사철 \n다양한 해산물과 어류를 이용한 음식이 많다.\n━\n자리돔물회  |  갈치국  |  성게국  |  한치(오징어)\n물회  |  옥돔구이  |  빙떡  |  고기국수\n제주7대 특산물\n제주도의 특산물은 청정 푸른 바다와 \n비옥한 대지에서 생산 되기에 제주에 유일\n하거나 타 지역 특산물보다 우위의 품질을 \n자랑한다.\n━\n감귤 | 고사리 | 자리돔 | 옥돔 | 갈치 | 말고기 | \n돼지고기\n제주7대 도로\n제주가 자랑하는 바람과 바다, 그리고 \n한라산과 올망졸망한 오름군락을 배경으로\n달리는 도로여행. 제주여행의 색다른 재미를\n더한다.\n━\n5·16도로 | 애월~하귀 해안도로 | 사계 해안\n도로 | 세화녹산장선 도로 | 비자림로 | 월정리\n해안도로 | 1100도로\n제주7대 체험거리","page":5,"trapped":"/False","source":"./data/제주관광가이드.pdf","creator":"Adobe InDesign 21.0 (Windows)","page_label":"6","total_pages":42,"creationdate":"2026-03-12T11:22:44+09:00"}


created_at: 2026-06-01 04:13:05
topic: persistent://default/default/80d8d54a-809f-4b2e-89e9-7bb042a24d9e
id: 8649f5a7-e6ad-46cf-a9ed-e47a8c170552
encoding: FLOAT32
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{"total_pages":42,"producer":"Adobe PDF Library 18.0","moddate":"2026-03-12T11:30:50+09:00","page_label":"6","creationdate":"2026-03-12T11:22:44+09:00","page":5,"trapped":"/False","creator":"Adobe InDesign 21.0 (Windows)","source":"./data/제주관광가이드.pdf","chroma:document":"해안도로 | 1100도로\n제주7대 체험거리\n체험은 색다른 경험과 스릴을 안겨준다. 제주여행에서의 기분 좋은 추억을 만들어 줄 것이다.\n━\n한라산·오름 등반 | 올레길·숲길 걷기 | 청정농촌(감귤 따기, 고사리 꺾기)체험 | 해양레저 \n(잠수함, 유람선, 요트, 배 낚시)체험 | 해안도로 드라이브, 자전거·스쿠터 체험 | 섬 속의 섬 여행 | \n승마 체험\n제주7대 건축\n세상에서 가장 아름다운 것은 자연이다. \n그속에 자연을 거스르지 않고 조화를 이루며\n서 있는 제주건축은 또하나의 새로운 볼\n거리를 만들어 낸다.\n━\n제주현대미술관 | 제주월드컵경기장 | 다음\n커뮤니케이션 본사 | 포도호텔 | 해심헌 | \n성읍민속마을 | 제주목관아\n제주 7대 명품\n원더스 제주! 세계가 인정한 제주의 가치 ‘세계인의 보물섬 제주’\n세계7대불가사의’ 로 알려진 ‘뉴세븐원더스(The New 7wonders)’ 재단은\n전 세계인의 투표를 통해 세계에서 가장 아름다운 자연경관 7곳을"}


created_at: 2026-06-01 04:13:05
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{"source":"./data/제주관광가이드.pdf","trapped":"/False","creationdate":"2026-03-12T11:22:44+09:00","total_pages":42,"page":5,"chroma:document":"전 세계인의 투표를 통해 세계에서 가장 아름다운 자연경관 7곳을 \n세계7대자연경 관으로 선 정하였습니다 . 이 에 따라  201 1 년 1 1 월, 제주 도는 \n세계7대자연경관으로  최종 선정되었습니다.\n생물권보전지역, 세계자연유산, 세계지질공원으로 지정된 제주도! \nUNESCO가 인증하는 3관왕을 달성한 지역으로 전 세계인이 함께 가꾸고 보전해야 \n할 환경자산의 보물섬입니다.\nUNESCO 생물권보전지역(2002)\n대 상 지 역│한라산국립공원 천연기념물(천연보호구역)로 지정된 영천, 효돈천, 문섬, \n범섬, 섶섬\nUNESCO 세계자연유산(2007)\n대 상 지 역│한라산 천연보호구역, 성산일출봉, 거문오름용암동굴계 (거문오름, 벵뒤굴, \n만장굴, 김녕사굴, 당처물동굴, 용천동굴등)\nUNESCO 세계지질공원(2010)\n대 상 지 역│제주도 전체, 한라산, 만장굴, 성산일출봉, 서귀포패류화 석층, 천지연폭포, \n중문대포주상절리대, 산방산, 용머리해안, 수월봉, 우도, 비양도, 선흘곶자왈","producer":"Adobe PDF Library 18.0","moddate":"2026-03-12T11:30:50+09:00","creator":"Adobe InDesign 21.0 (Windows)","page_label":"6"}


created_at: 2026-06-01 04:13:05
topic: persistent://default/default/80d8d54a-809f-4b2e-89e9-7bb042a24d9e
id: 7add1444-03ae-47fa-a313-5251b4c7a77f
encoding: FLOAT32
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{"page_label":"6","producer":"Adobe PDF Library 18.0","chroma:document":"중문대포주상절리대, 산방산, 용머리해안, 수월봉, 우도, 비양도, 선흘곶자왈\n1. South America 남아메리카 아마존\n2. Halong Bay 베트남 하롱베이 \n3. Iguazu Falls 아르헨티나, 브라질 이과수폭포\n4. Jeju Island 대한민국 제주도\n5. Komodo 인도네시아 코모도 국립공원 \n6. Puerto Princesa 필리핀 푸에르토 프린세사 지하강 \n7. Table Mountain 남아프리카 테이블 산\n유네스코 3관왕 제주 \n7대  자연경관 제주\n08JEJU   ISL AND   POCKET   BOOK —09\nAbout  Jeju","source":"./data/제주관광가이드.pdf","creator":"Adobe InDesign 21.0 (Windows)","page":5,"moddate":"2026-03-12T11:30:50+09:00","creationdate":"2026-03-12T11:22:44+09:00","trapped":"/False","total_pages":42}


created_at: 2026-06-01 04:13:05
topic: persistent://default/default/80d8d54a-809f-4b2e-89e9-7bb042a24d9e
id: 02e31ceb-8ae2-450e-9ed2-afaebdc0a649
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{"page":6,"source":"./data/제주관광가이드.pdf","producer":"Adobe PDF Library 18.0","trapped":"/False","chroma:document":"초가을에 집중적으로 내리는 가을비를 \n제외하면 습하지 않고 쾌청하다. \n밤낮으로 큰 일교차에 대비해 가벼운 \n겉옷을 준비하는 게 좋다.\n단풍으로 물들고 억새가 흐드러진 길을 \n시간 간 줄 모른 채 걷게 될 것이다.\n월별 최고/최저기온\n9월 10월 11월\n29.5℃ ~ 16.9℃ 27.9℃ ~ 13.1℃ 23.4℃ ~ 7℃\n평균  24.1℃ 평균  20℃ 평균  15.6℃\n내륙보다 포근하여 영하 기온으 로 \n내려가는 날이 비교적 적다. \n바람이 매섭고 잦아, 체감온도는 더욱 \n떨어지는 경우가 많다. \n눈꽃 트레킹을 떠난다면 방한용품 \n준비를 단단히 해야 한다\n월별 최고/최저기온\n12월 1월 2월\n19.8℃ ~ 3.4℃ 14.7℃ ~ 2℃ 17.8℃ ~ 2.2℃\n평균  10.7℃ 평균  7.8℃ 평균  9.3℃\n제주의 가을 9월~11월\n 제주의 겨울 12월~2월\n제주의 날씨와 환경\n포근한 공기와 따뜻한 햇살이 함께해 \n여행의 최적기로 꼽힌다. \n매화, 유채꽃, 벚꽃 등 모두 나열할 수","total_pages":42,"moddate":"2026-03-12T11:30:50+09:00","creationdate":"2026-03-12T11:22:44+09:00","creator":"Adobe InDesign 21.0 (Windows)","page_label":"7"}


created_at: 2026-06-01 04:13:05
topic: persistent://default/default/80d8d54a-809f-4b2e-89e9-7bb042a24d9e
id: 30a6a05b-5fa4-4eac-aac7-28dad7d74cf9
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{"total_pages":42,"chroma:document":"여행의 최적기로 꼽힌다. \n매화, 유채꽃, 벚꽃 등 모두 나열할 수 \n없을 만큼 많은 생명들이 움트는 제주의 봄. \n일교차가 심한 날들이 많으니 얇은 \n겉옷이나 스카프 등을 반드시 챙기도록 하자.\n월별 최고/최저기온\n3월 4월 5월\n21.9℃ ~ 4.2℃ 24.9℃ ~ 5.4℃ 29.7℃ ~12.1℃\n평균11.6℃ 평균 15℃ 평균 19.9℃\n내리쬐는 햇볕과 시원한 바다의 풍경이 \n어우러지는 계절이다. \n습하며 장마로 인한 폭우나 태풍이 \n오는 경우가 있어 여행 중 주의가 필요하다. \n여름을 만끽할 수 있는 바다, 폭포, 숲 등 \n제주의 여름은 더욱 반짝인다.\n월별 최고/최저기온\n6월 7월 8월\n28.8℃ ~ 15.6℃ 30.5℃ ~ 18.6℃ 32.2℃ ~ 20℃\n평균  21.7℃ 평균  24.2℃ 평균  27.1℃\n대한민국 최고의 휴양지인 제주도는 내륙에 비해 대체로 포근한 편이지만, 환절기에는 기온차가 \n크고 여름에는 높은 습도를 자랑하는 변화무쌍한 섬이다.","moddate":"2026-03-12T11:30:50+09:00","page_label":"7","trapped":"/False","producer":"Adobe PDF Library 18.0","creator":"Adobe InDesign 21.0 (Windows)","creationdate":"2026-03-12T11:22:44+09:00","source":"./data/제주관광가이드.pdf","page":6}


created_at: 2026-06-01 04:13:05
topic: persistent://default/default/80d8d54a-809f-4b2e-89e9-7bb042a24d9e
id: 2d7394f8-6994-4591-95de-e5c120a98436
encoding: FLOAT32
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{"creationdate":"2026-03-12T11:22:44+09:00","producer":"Adobe PDF Library 18.0","page_label":"7","total_pages":42,"page":6,"trapped":"/False","chroma:document":"크고 여름에는 높은 습도를 자랑하는 변화무쌍한 섬이다.\n제주의 봄 3월~5월\n 제주의 여름 6월~8월\n2025년 서귀포기준(기상청국립기후데이터센터)\n10JEJU   ISL AND   POCKET   BOOK —11\nAbout  Jeju","creator":"Adobe InDesign 21.0 (Windows)","moddate":"2026-03-12T11:30:50+09:00","source":"./data/제주관광가이드.pdf"}


created_at: 2026-06-01 04:13:05
topic: persistent://default/default/80d8d54a-809f-4b2e-89e9-7bb042a24d9e
id: 9f1eb677-c815-4405-a9e7-dbe83e0a78a3
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{"chroma:document":"제주 교통 안내\n제주도의 모든 버스에서는 T-money 교통카드를 사용할 수 있다. 카드 \n또는 열쇠고리 등의 형태로 제작된 T-money를 충전한 후 버스에 달린 \nT-money 단말기에 접촉하면 바로 승차 금액이 지불되는 시스템이다. \nT-money 로고가 붙어있는 편의점 등에서 교통카드 구입과 충전이 \n가능하며, 편리하게 제주도 내 대중교통을 이용할 수 있다.\n제주도의 육상 대중교통은 버스를 기본으로 하고 택시가 이를 보충하는 형태이며 지하철이나 \n기차는 없다. 여행자의 경우는 렌터카를 이용하는 비율이 높다. 렌터카는 편리한 길안내를 \n해주는 내비게이션 시스템을 포함하여 저렴한 가격에 빌릴 수 있는 곳이 제주국제공항과 \n제주국제여객터미널 주변에 있다. 단 렌터카 수요가 집중되는 7, 8월에는 평소보다 훨씬 비싸다. \n외국인일 경우 렌터카 사용을 위해 ‘국제면허증’ 과 ‘여권’ 이 꼭 필요하다.\n티머니\n시내버스 요금 1,200원 (교통카드 사용 시 50원 할인)","total_pages":42,"page":7,"producer":"Adobe PDF Library 18.0","moddate":"2026-03-12T11:30:50+09:00","trapped":"/False","source":"./data/제주관광가이드.pdf","page_label":"8","creator":"Adobe InDesign 21.0 (Windows)","creationdate":"2026-03-12T11:22:44+09:00"}


created_at: 2026-06-01 04:13:05
topic: persistent://default/default/80d8d54a-809f-4b2e-89e9-7bb042a24d9e
id: 6b89538e-c5a6-4b6f-bb05-70de686ddfbe
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{"total_pages":42,"page_label":"8","chroma:document":"티머니\n시내버스 요금 1,200원 (교통카드 사용 시 50원 할인)\n택시 요금 기본요금 4,300원, 126m 또는 31초 당 100원 \n렌터카 요금 시기, 차종, 회사마다 상이\n버스나 택시에 승차했는데 운전기사가 외국어를 구사하지 못한다면 통역서비스를 이용하거나 \n이 책에서 목적지를 안내한 페이지를 보여주자. 큰 도움이 될 것이다.\nBBB 전화통역 서비스 1588-5644 글로벌 택시 이용 문의 1899-4314\n제주관광정보센터 064-740-6000 (한/영/일/중 연중무휴 09:00~18:00)\n주요 도로 소개\n제주도의 모든 길은 아름답다. 바다를 풍경 삼아 달릴 수 있는 해안도로와 계절마다 다른 한라산의\n풍경이 반기는 중산간로까지. 이동하는 시간마저 힐링의 시간이 될 것이다. 풍경에 취해 과속은 \n금물, 안전 운전으로 제주도를 만끽하자.\n1131\n11311135\n1135\n11171117\n1139\n1139\n97\n97\n1115\n1115\n1115\n1118\n1118\n1132","producer":"Adobe PDF Library 18.0","page":7,"creator":"Adobe InDesign 21.0 (Windows)","creationdate":"2026-03-12T11:22:44+09:00","source":"./data/제주관광가이드.pdf","moddate":"2026-03-12T11:30:50+09:00","trapped":"/False"}


created_at: 2026-06-01 04:13:05
topic: persistent://default/default/80d8d54a-809f-4b2e-89e9-7bb042a24d9e
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{"source":"./data/제주관광가이드.pdf","trapped":"/False","creator":"Adobe InDesign 21.0 (Windows)","moddate":"2026-03-12T11:30:50+09:00","total_pages":42,"producer":"Adobe PDF Library 18.0","page":7,"chroma:document":"1139\n1139\n97\n97\n1115\n1115\n1115\n1118\n1118\n1132\n1132\n1132\n1132\n1132\n1132\n1132\n일주도로\n해안을 따라 제주도를 순환하는 도로이다. 해안을 따라 이어져 해안 경치를 즐기기에 좋다.\n1135 평화로\n서귀포시 대정읍과 제주시 애월읍을 잇는 도로이다. 안개가 잦은 구간이 있으니 주의할 것. \n1131 516도로\n한라산을 가로질러 제주시와 서귀포시를 잇는 도로이다. 숲터널 등 아름다운 코스가 많지만 \n커브가 험해 초보 운전자들은 주의해야 한다. 눈내리는 겨울에는  폭설을 주의해야 한다.\n1139 1100도로\n한라산을 넘어 제주시에서 서귀포의 중문관광단지로 연결되는 도로. 고도 1100m 지점을 \n지나서 1100도로라고 한다. 516도로와 마찬가지로 가파르고 굽이가 많아 조심해야 한다. \n1118 남조로\n조천, 함덕을 지나 교래, 송당, 남원까지 이어지는 도로이다. 도로 상태가 좋고 일자로 쭉 뻗은","creationdate":"2026-03-12T11:22:44+09:00","page_label":"8"}


created_at: 2026-06-01 04:13:05
topic: persistent://default/default/80d8d54a-809f-4b2e-89e9-7bb042a24d9e
id: 60987081-ff3a-405a-bb7d-b8d9f4d539d3
encoding: FLOAT32
metadata:
{"total_pages":42,"creationdate":"2026-03-12T11:22:44+09:00","moddate":"2026-03-12T11:30:50+09:00","trapped":"/False","creator":"Adobe InDesign 21.0 (Windows)","producer":"Adobe PDF Library 18.0","page":7,"page_label":"8","chroma:document":"숲길이 많다. 과속에 주의하며 경치를 즐기도록 하자.\n1117 제1산록도로\n애월읍에서 아라동을 연결하는 도로이다. 계절의 변화를 바로 볼 수 있는 풍경이 매우 \n뛰어나다.\n1115 제2산록도로\n제주시 한경면과 서귀포시 상효동을 연결하는 도로. 쭉 뻗은 직선도로를 따라 한라산의 \n우수한 경관을 자랑한다.\n97 번영로\n제주시와 표선면을 잇는 도로이다. 주변 풍경이 아름다워 드라이브 하기 좋다.\n12JEJU   ISL AND   POCKET   BOOK —13\nAbout  Jeju","source":"./data/제주관광가이드.pdf"}


created_at: 2026-06-01 04:13:05
topic: persistent://default/default/80d8d54a-809f-4b2e-89e9-7bb042a24d9e
id: 3ce638c3-bebc-4766-9c2b-71518856f549
encoding: FLOAT32
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{"moddate":"2026-03-12T11:30:50+09:00","trapped":"/False","page":8,"creator":"Adobe InDesign 21.0 (Windows)","page_label":"9","producer":"Adobe PDF Library 18.0","chroma:document":"렌터카는 제주를 여행하는 여행객들이 가장 많이 이용하는 교통수단이다. 구석구석 아름다운 \n제주를 만끽하는 데 있어 렌터카는 정말 유용하다. 포근한 제주의 바람을 맞으며 푸른 바다와 \n초록 숲을 달려보자.\n업체 선정\n제주도에는 많은 렌터카 업체가 있다. 비용 또한 상이하며 공항에서 \n바로 픽업하거나 셔틀버스로 이동해서 픽업하는 경우 등 업체별로 \n다른 서비스를 제공한다. 무조건 저렴한 곳으로 결정하는 것보다는 \n꼼꼼히 후기를 살펴보고 렌터카 업체를 선정하는 것이 좋다.\n보험\n즐거운 여행에서 사고는 있어서는 안되겠지만 항상 만약에 대비해야 하는 법. 특히 초행길이 많은 \n여행자들에게 보험은 선택이 아닌 필수다. 사고 발생 시 차량 수리비와 보상료 등을 청구하게 \n되는데 ‘슈퍼자차’ 또는 ‘완전면책’ 보험에 가입하면 해결된다. \n※보험에 가입했더라도 업체마다 규정이 상이하므로 한도 금액 등 약관을 꼼꼼히 확인하자.\n차량 점검\n차량을 인수할 때 꼼꼼한 확인은 필수. 작은 흠집","total_pages":42,"creationdate":"2026-03-12T11:22:44+09:00","source":"./data/제주관광가이드.pdf"}


created_at: 2026-06-01 04:13:05
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{"moddate":"2026-03-12T11:30:50+09:00","creator":"Adobe InDesign 21.0 (Windows)","trapped":"/False","producer":"Adobe PDF Library 18.0","source":"./data/제주관광가이드.pdf","page":8,"chroma:document":"차량 점검\n차량을 인수할 때 꼼꼼한 확인은 필수. 작은 흠집 \n등은 문제 없으나 눈에 띄는 상처나 특징은 미리 사진 \n촬영을 해두는 것이 좋다. 계기판과 시동 상태, 연료 \n역시 확인하고 궁금한 점은 꼭 직원에게 문의하여 \n즐거운 여행을 준비하자.\n내비게이션 애플리케이션 준비\n렌터카 업체에서 기본적으로 제공하는 내비게이션이 있지만 \n신설되는 관광지나 업체 등은 잘 나오지 않는 경우도 있다. \n이럴 때는 업데이트가 빠른 스마트폰의 애플리케이션을 \n이용하는 것이 좋다. 미리 다운로드해 두 자. \n제주운전 팁\n제주 시내 중심을 제외하고는 복잡한 곳이 없어 운전하는 것이 어렵지 않은 편이다. 제주는 회전 \n교차로가 많으므로 들어가고 나가는 방향을 잘 확인하자. 동물, 스쿠터가 갑자기 튀어나올 수 있으니 \n도로 및 마을 안 한적한 곳에서도 속도를 줄이자. 한라산 올라가는 길만 주의하면 비교적 도로 \n여건은 좋은 편이다.\n스쿠터\n렌터카는 부담스럽고 대중교통은 불편하게 느껴진다면","total_pages":42,"page_label":"9","creationdate":"2026-03-12T11:22:44+09:00"}


created_at: 2026-06-01 04:13:05
topic: persistent://default/default/80d8d54a-809f-4b2e-89e9-7bb042a24d9e
id: fcabfdb8-0a85-48c7-a3ac-9180fe8183d2
encoding: FLOAT32
metadata:
{"moddate":"2026-03-12T11:30:50+09:00","chroma:document":"여건은 좋은 편이다.\n스쿠터\n렌터카는 부담스럽고 대중교통은 불편하게 느껴진다면  \n스쿠터를 대여하는 방법도 있다. 렌터카 보다 저렴하고 이동과 \n주차 등이 편리하며 무엇보다 시원한 바람과 자연을 직접 느낄 \n수 있다. 하지만 겨울이나 장마철 등 날씨 상황에 따라 위험한 \n운행이 될 수도 있고 무엇보다 사고에 취약하기 때문에 스쿠터에 \n익숙하지 않은 여행자라면 피하는 것이 좋다.\n렌터카\n14JEJU   ISL AND   POCKET   BOOK —15\nAbout  Jeju","page":8,"trapped":"/False","creator":"Adobe InDesign 21.0 (Windows)","source":"./data/제주관광가이드.pdf","page_label":"9","total_pages":42,"creationdate":"2026-03-12T11:22:44+09:00","producer":"Adobe PDF Library 18.0"}


created_at: 2026-06-01 04:13:05
topic: persistent://default/default/80d8d54a-809f-4b2e-89e9-7bb042a24d9e
id: 8cbcb76f-b99e-487f-97e3-d92c07672444
encoding: FLOAT32
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{"creator":"Adobe InDesign 21.0 (Windows)","moddate":"2026-03-12T11:30:50+09:00","page":9,"total_pages":42,"chroma:document":"제주특별자치도와 개인·일반 택시운송사업조합에서 \n보증 및 관리하는 택시로, 렌터카 이용은 어렵지만 \n편안한 여행을 생각하는 여행객에게는 일정 동안 \n택시를 대절해 관광을 즐길 수 있는 택시투어를 \n추천한다. 풍부한 운전 경력의 기사들의 안내로 \n편안하게 제주를 여행할 수 있도록 도와준다. \n택시 이용은 육지와 다르지 않다. 시내권에 있는 것이 \n아니라면 콜택시나 카카오T 서비스를 이용하는 것이 \n신속하다. \n기본요금\n중형 대형\n4,300원 6,000원\n제주관광행복택시\n택시 1,200원으로 제주 전 지역 어디든지!\n제주 전 지역 시내버스화를 통해 1,200원이면\n제주 전 지역으로 이동 가능. 교통카드를 \n이용하면 1,150원으로 이용 가능 (급행버스는 \n2,000원에서 최대 3,000원)\n급행버스 8개 노선, 제주공항 기•종점 운행\n제주국제공항과 도내 모든 환승정류장을 연결\n하는 급행노선이 신설되어 공항에서 출발해서 \n도내 전 지역을 1시간 내외로 이동 가능","creationdate":"2026-03-12T11:22:44+09:00","producer":"Adobe PDF Library 18.0","page_label":"10","trapped":"/False","source":"./data/제주관광가이드.pdf"}


created_at: 2026-06-01 04:13:05
topic: persistent://default/default/80d8d54a-809f-4b2e-89e9-7bb042a24d9e
id: 90c7454f-e4e4-4069-8b82-55f74e10b797
encoding: FLOAT32
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{"chroma:document":"하는 급행노선이 신설되어 공항에서 출발해서 \n도내 전 지역을 1시간 내외로 이동 가능\n대중교통 우선차로제로 시내 관광도 빠르게!\n대중교통 차량만 이용 가능한 중앙차로제와 \n가변차로제로 막히는 제주시내 관광도 교통체증\n없이 빠르고 쾌적하게 이용 가능\n버스 증차, 무료 Wi-Fi 제공\n모든 버스에서 무료 Wi-Fi가 제공되어 스마트한\n여행 가능\n2017년부터 전면 개편된 제주의 버스. 급행, 간선,  지선 등 용도에 따른 노선으로 더욱 신속하게 \n이동할 수 있다. 환승 제도는 여행의 편의를 더한다.\n급행버스\n지역 간 주요 거점을 빠르게 이동. 주요 관광지와 지역 위주로 정차한다.\n간선버스\n세부화된 각종 노선으로 도심 내부와 외곽지역까지 장거리 운행한다.\n지선버스\n특정지역 내부를 단거리 운행. 제주시와 서귀포시 인근 마을을 운행한다.\n관광지순환버스\n810, 820번 버스로 각각 동부와 서부 주요 관광지를 순환한다.\n버스\n제주관광 행복택시\n1899-7321","creator":"Adobe InDesign 21.0 (Windows)","producer":"Adobe PDF Library 18.0","source":"./data/제주관광가이드.pdf","page_label":"10","page":9,"moddate":"2026-03-12T11:30:50+09:00","total_pages":42,"creationdate":"2026-03-12T11:22:44+09:00","trapped":"/False"}


created_at: 2026-06-01 04:13:05
topic: persistent://default/default/80d8d54a-809f-4b2e-89e9-7bb042a24d9e
id: f9e94188-82ea-4bfb-9e05-fea1c808da9d
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{"total_pages":42,"creator":"Adobe InDesign 21.0 (Windows)","page_label":"10","chroma:document":"버스\n제주관광 행복택시\n1899-7321\n16JEJU   ISL AND   POCKET   BOOK —17\nAbout  Jeju","moddate":"2026-03-12T11:30:50+09:00","trapped":"/False","creationdate":"2026-03-12T11:22:44+09:00","source":"./data/제주관광가이드.pdf","producer":"Adobe PDF Library 18.0","page":9}


created_at: 2026-06-01 04:13:05
topic: persistent://default/default/80d8d54a-809f-4b2e-89e9-7bb042a24d9e
id: 4d945c4f-0bc7-4b35-9e73-d9d61e9a0118
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{"trapped":"/False","page":10,"moddate":"2026-03-12T11:30:50+09:00","creator":"Adobe InDesign 21.0 (Windows)","source":"./data/제주관광가이드.pdf","page_label":"11","producer":"Adobe PDF Library 18.0","total_pages":42,"creationdate":"2026-03-12T11:22:44+09:00","chroma:document":"대천환승센터를 출발해 다랑쉬오름, 용눈이오름, 거문오름 등 \n제주의 대표 오름을 연결하며 달린다.\n아부오름제주세계자연유산 센터,\n거문오름\n거슨새미오름, 안돌오름, \n밧돌오름, 민오름\n선흘2리마을선녀와나무꾼선인동마을\n다희연 \n알밤오름\n동백동산습지센터\n810-2 810-1\n한울랜드\n어대오름 덕천리마을둔지오름 메이즈랜드비자림 다랑쉬오름입구(북)제주레일\n바이크\n용눈이오름\n다랑쉬오름 입구(남)\n손지오름\n송당리마을\n대천환승센터\n810\n제주동부\n동광환승센터에서 출발해 신화역사공원, 제주항공우주박물관, \n제주오설록 티뮤지엄, 저지오름 등을 거쳐 동광으로 돌아온다.\n산양곶자왈저지리사무소청수마을회관제주평화박물관저지문화예술인마을, 제주현대미술관, \n김창열도 립미술관생각하는정원환상숲곶자왈공원제주유리의성\n제주오설록\n티뮤지엄\n제주항공우주호텔\n제주항공우주박물관\n신화역사공원 헬로키티아일랜드세계자동차박물관서광동보건진료소광해악입구\n서광서리마을\n노리매\n구억리마을\n신평리마을\n동광환승센터\n820\n제주서부"}


created_at: 2026-06-01 04:13:05
topic: persistent://default/default/80d8d54a-809f-4b2e-89e9-7bb042a24d9e
id: 844ab31d-f0c6-4532-8527-31dc661a718b
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{"moddate":"2026-03-12T11:30:50+09:00","page":10,"page_label":"11","creator":"Adobe InDesign 21.0 (Windows)","creationdate":"2026-03-12T11:22:44+09:00","trapped":"/False","total_pages":42,"chroma:document":"서광서리마을\n노리매\n구억리마을\n신평리마을\n동광환승센터\n820\n제주서부\n820-2 820-1\n렌터카 이용이 부담스러운 여행자라면 관광지 순환버스를 주목하자. 제주 동부와 서부지역의 \n주요 관광지와 오름 등 그야말로 알짜 관광지를 순환하는 친절한 버스다. 성인 기준 3,000원의 \n1일 이용권을 구입하면 하루 동안 관광지 순환버스를 무제한으로 이용 가능하다. 이제 버스만 \n타고도 제주의 숨겨진 속살을 마음껏 만나보자.\n이용요금 : 1,200원(성인), 900원(청소년), 400원(어린이) \n                    (1회 승차 기준, 교통카드 사용 시 50원 할인)\n1일 이용권(ONE DAY PASS) : 버스 탑승 후 기사에게 구입\n성인 청소년 어린이\n3,000원 2,000원 1,000원\n※ 정액권 이용시 각 노선내 관광지 할인 혜택(입장료할인)\n문의 : 0 6 4 -74 6 -7310\n810\n첫차08:20 / 막차 18:00\n820\n첫차09:00 / 막차 17:00","source":"./data/제주관광가이드.pdf","producer":"Adobe PDF Library 18.0"}


created_at: 2026-06-01 04:13:05
topic: persistent://default/default/80d8d54a-809f-4b2e-89e9-7bb042a24d9e
id: 611058a0-db28-432f-9c88-353403eeb309
encoding: FLOAT32
metadata:
{"moddate":"2026-03-12T11:30:50+09:00","total_pages":42,"creator":"Adobe InDesign 21.0 (Windows)","producer":"Adobe PDF Library 18.0","source":"./data/제주관광가이드.pdf","chroma:document":"810\n첫차08:20 / 막차 18:00\n820\n첫차09:00 / 막차 17:00\n-1 08:20, 09:20 (공항 -> 대천) \n-1 09:00, 17:00 (동광 -> 공항)\n-2 08:20, 09:20 (공항 -> 동광)\n-2 18:10, 19:10  (대천 -> 공항) -3\n07:57 (제주도청->영어교육도시)\n18:22 (영어교육도시->제주도청)\n관광지 순환버스\n이용안내\n방림원\n820-3은 제주도청(신제주로터리)를 출발해 한라병원, 동광환승정류장, 신화역사공원, 오설록, 영어교육도시(국제교회)를 거쳐 운행한다.\n18JEJU   ISL AND   POCKET   BOOK —19\nAbout  Jeju","trapped":"/False","page_label":"11","page":10,"creationdate":"2026-03-12T11:22:44+09:00"}


created_at: 2026-06-01 04:13:05
topic: persistent://default/default/80d8d54a-809f-4b2e-89e9-7bb042a24d9e
id: 86324ba4-a8fa-4281-9e2c-17360a24a2b2
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{"trapped":"/False","chroma:document":"이용요금\n1회 이용권 : 성인, 소인 및 청소년 : 5,000원 / 6세 미만 : 무료\n1일 이용권\n일반 소인, 청소년\n12,000원 8,000원\n각 정류장마다 탑승시 결제(현금, 교통카드, 신용카드), 장애인 및 국가유공자 6,000원, 6세 미만 무료\n운행시간\n09:00 ~ 19:30(막차 17:00 출발) / 2시간 소요/공항에서는 매 시간 30분에 출발\n배차시간은 1시간 간격, 1일 9회 운행\n* 매주 월요일 휴차, 운행일정 및 코스는 현지 사정에 따라, 변경될 수 있음.\n문의전화\n064-741-8784~5(시티투어팀)\n제주시티투어버스\n홈페이지 바로가기\n제주시티투어는 탑승권 한 장으로 자유롭게 타고 내리며 제주시 곳곳을 여행할 수 있는 흥미로운 \n순환형 투어 프로그램이다. 2층 버스를 타고 제주시의 주요 관광지 뿐만 아니라 제주국제공항과  \n터미널 등 교통의 요지를 두루 거치며 전통재래시장 및 해안도로를 연계하는 코스로 구성돼 있다.\n제주시티투어버스\n이용안내\n순환형코스\n2","source":"./data/제주관광가이드.pdf","moddate":"2026-03-12T11:30:50+09:00","creator":"Adobe InDesign 21.0 (Windows)","producer":"Adobe PDF Library 18.0","page_label":"12","creationdate":"2026-03-12T11:22:44+09:00","total_pages":42,"page":11}


created_at: 2026-06-01 04:13:05
topic: persistent://default/default/80d8d54a-809f-4b2e-89e9-7bb042a24d9e
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{"trapped":"/False","creationdate":"2026-03-12T11:22:44+09:00","creator":"Adobe InDesign 21.0 (Windows)","page":11,"source":"./data/제주관광가이드.pdf","total_pages":42,"producer":"Adobe PDF Library 18.0","chroma:document":"제주시티투어버스\n이용안내\n순환형코스\n2\n3 19\n8 14\n9 13\n20\n12\n21\n10 11\n7\n6\n15\n4 18\n5\n16\n17\n제주항국제여객터미널\n1\n제주국제공항\n김만덕기념관\n동문시장\n제주시버스터미널\n삼무공원(에어시티호텔)\n메종글래드호텔(신라면세점)\n노형오거리(드림타워)\n한라수목원\n월랑마을(베스트웨스턴호텔)\n관덕정(제주목관아)\n용연구름다리(용두암)\n용담레포츠공원\n제주항연안여객터미널 사라봉(이화아파트) 제주민속자연사박물관\n어영공원(어영마을)도두봉(무지개해안도로)이호목마등대(이호테우해수욕장)제주시민속오일시장연동한일시티파크(롯데시티호텔)\n20JEJU   ISL AND   POCKET   BOOK —21\nAbout  Jeju","page_label":"12","moddate":"2026-03-12T11:30:50+09:00"}


created_at: 2026-06-01 04:13:05
topic: persistent://default/default/80d8d54a-809f-4b2e-89e9-7bb042a24d9e
id: 204e8fb7-6705-4d90-94e0-99821f174d36
encoding: FLOAT32
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{"creator":"Adobe InDesign 21.0 (Windows)","page":12,"creationdate":"2026-03-12T11:22:44+09:00","producer":"Adobe PDF Library 18.0","trapped":"/False","source":"./data/제주관광가이드.pdf","page_label":"13","moddate":"2026-03-12T11:30:50+09:00","chroma:document":"생활 속 제주어\n제주는 타 지역보다 한국어의 고형(古形)을 많이 유지하고 있는 동시에 \n제주도만의 고유한 어휘나 문법적 특성을 가지고 있다.\n다른 지역 사람이 못 알아듣는 제주어\n제주어 뜻풀이\n솔쩨기 살짝\n안네다 드리다\n베지근허다 입안에 기름기가 감돌아 맛이 있다.\n엄불랑허다 어마어마하다\n코시롱허다 고소하다\n산도록허다 시원하다  예) 물이 산도록헌 게 좋다.\n두령청이 우두망찰\n무사 왜\n영, 경, 정 이렇게, 그렇게, 저렇게\n게메 글쎄\n인사말\n제주어 뜻풀이\n펜안허우꽈? 편안(안녕)하십니까?\n제주도 오난 어떵허우꽈? 제주도에 오니 어떠십니까?\n차말로 좋수다. 참말로 좋습니다.\n공기도 마고, 산이영 바다잉여 마딱 좋은게마씀 공기도 맑고, 산이랑 바다랑 모두 좋네요.\n서울 갈 때랑 하영 다앙 갑서. 서울 갈 때는 많이 담아서 가십시오.\n게메양. 경 헤시민 얼마나 좋코마씀? 글쎄요. 그렇게 했으면 얼마나 좋겠습니까?\n식당에서\n제주어 뜻풀이\n무신걸 먹으코? 무엇을 먹을까?","total_pages":42}


created_at: 2026-06-01 04:13:05
topic: persistent://default/default/80d8d54a-809f-4b2e-89e9-7bb042a24d9e
id: 641f15d0-928f-4377-a5e8-603ce6501f7a
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{"page":12,"producer":"Adobe PDF Library 18.0","chroma:document":"식당에서\n제주어 뜻풀이\n무신걸 먹으코? 무엇을 먹을까?\n게메양, 제주도에만 이신 거 먹게마씀. 글쎄요, 제주도에만 있는 것 먹게요.\n구젱기영 무꾸럭이영 오토미영 하간 거 다 잇수다. 소라랑 문어랑 옥돔이랑 온갖 거 다 있습니다.\n모멀펌벅에 자리젯! 메밀범벅에 자리젓!\n먹을 것도 잘도 하신게. 먹을 것도 정말(엄청) 많네.\n타 지역과 의미가 다른 제주어\n제주어 뜻풀이\n감저 고구마\n지슬, 지실 감자\n-허게 하자(청유의 의미) 공부허게: 공부하자\n글라 가자 예) 장에 글라.(장에 가자.)\n호미 낫\n가겡이, 가각지, 가게 호미\n폭삭 속앗수다 무척 수고하셨습니다.\n삼춘 ①삼촌    ②나이 든 남자 어른이나 여자 어른을 부르는 말.\n요망지다 야무지다\n아 까다 아깝다(사랑스럽고 귀엽다)\n젊은 층도 자주 사용하는 제주어\n제주어 뜻풀이\n뭐허멘? 뭐하니?\n가이, 자이, 야이 걔, 쟤, 얘\n기 그래\n뒌 됐어\n허쿠다 하겠습니다\n잘도 정말, 엄청\n벨라지다 바라지다","total_pages":42,"trapped":"/False","creator":"Adobe InDesign 21.0 (Windows)","moddate":"2026-03-12T11:30:50+09:00","source":"./data/제주관광가이드.pdf","page_label":"13","creationdate":"2026-03-12T11:22:44+09:00"}


created_at: 2026-06-01 04:13:05
topic: persistent://default/default/80d8d54a-809f-4b2e-89e9-7bb042a24d9e
id: 698e3241-1823-4f02-881c-5050092086c2
encoding: FLOAT32
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{"page":12,"producer":"Adobe PDF Library 18.0","trapped":"/False","creator":"Adobe InDesign 21.0 (Windows)","creationdate":"2026-03-12T11:22:44+09:00","source":"./data/제주관광가이드.pdf","total_pages":42,"moddate":"2026-03-12T11:30:50+09:00","chroma:document":"기 그래\n뒌 됐어\n허쿠다 하겠습니다\n잘도 정말, 엄청\n벨라지다 바라지다\n-멘  예) 나 일허멘. -고 있어  예)나 일 하고 있어. \n22JEJU   ISL AND   POCKET   BOOK —23\nCulture  Jeju","page_label":"13"}


created_at: 2026-06-01 04:13:05
topic: persistent://default/default/80d8d54a-809f-4b2e-89e9-7bb042a24d9e
id: c19ddd4e-7b10-40f7-94d2-af898a28e655
encoding: FLOAT32
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{"page":13,"creator":"Adobe InDesign 21.0 (Windows)","producer":"Adobe PDF Library 18.0","total_pages":42,"creationdate":"2026-03-12T11:22:44+09:00","chroma:document":"국수문화거리   제주시 이도이동 일원 -민속자연사박물관(삼성로 40) 검색┃제주시 이도동에는 국수를 \n좋아하는 제주 사람들은 물론 관광객들을 불러 모으는 국수문화거리가 있어 밤낮으로 푸짐한 국수를 파는 \n국숫집들이 사람들을 불러 모으고 있다.\n갈치요리                                                                                                                                            \n제주도에서 갈치를 먹는 방법은 다양하다. 갈치 본연의 \n맛을 느낄 수 있는 갈치구이. 갈치회는 비리지 않고 씹는 \n맛이 고소해 인기가 좋다. 싱싱한 갈치살이 입안에서 \n살살 녹는 조림 또한 갈치요리의 별미. 고춧가루로 양념한 \n조림은 밥에 쓱싹쓱싹 비벼 먹어도 맛있다. 맑은 국물에 \n늙은 호박을 넣어 끓인 갈칫국은 해장용으로도 그만이다.\n고기국수","moddate":"2026-03-12T11:30:50+09:00","page_label":"14","source":"./data/제주관광가이드.pdf","trapped":"/False"}


created_at: 2026-06-01 04:13:05
topic: persistent://default/default/80d8d54a-809f-4b2e-89e9-7bb042a24d9e
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encoding: FLOAT32
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{"source":"./data/제주관광가이드.pdf","trapped":"/False","creator":"Adobe InDesign 21.0 (Windows)","page":13,"moddate":"2026-03-12T11:30:50+09:00","chroma:document":"늙은 호박을 넣어 끓인 갈칫국은 해장용으로도 그만이다.\n고기국수\n뽀얗게 우려낸 육수에 면을 넣고, 삶은 돼지고기를 \n얹은 뒤, 입맛에 따라 청양고추와 고춧가루, 후춧가루 등을 \n넣고 먹으면 된다. 두툼한 중면에 수육까지 함께 \n먹으니 한 그릇만으로도 속을 든든하게 채울 수 있다. \n시원하면서도 배지근한 고기국수 국물은 해장용으로도 \n인기가 좋다.\n해물뚝배기\n작은 전복같은 ‘오분자기’를 비롯해 꽃게와 \n딱새우 등 해물을 푸짐하게 넣고 시원하게 \n끓여낸 해물뚝배기. 신선한 해물과 된장 \n육수의 조화로운 맛을 느낄 수 있다.\n갈치요리                                                                                                                                            \n자리젓\n제주 근해에서만 잡히고 있는 자리돔을 원료로 하여 염장한 것으로 비늘을 제거하여 먹기에 좋고","page_label":"14","total_pages":42,"producer":"Adobe PDF Library 18.0","creationdate":"2026-03-12T11:22:44+09:00"}


created_at: 2026-06-01 04:13:05
topic: persistent://default/default/80d8d54a-809f-4b2e-89e9-7bb042a24d9e
id: 8b0e432a-d1a1-4648-a5ef-7f3ba34616f6
encoding: FLOAT32
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{"trapped":"/False","source":"./data/제주관광가이드.pdf","page_label":"14","creationdate":"2026-03-12T11:22:44+09:00","producer":"Adobe PDF Library 18.0","moddate":"2026-03-12T11:30:50+09:00","total_pages":42,"page":13,"creator":"Adobe InDesign 21.0 (Windows)","chroma:document":"맛도 구수하다. 풋고추, 마늘 등 갖은양념을 곁들이면 밥반찬으로도 좋다. \n제주에서 꼭 맛봐야 할 음식\n흑돼지                                                                                                                                                \n제주의 흑돼지 음식점은 어딜 가나 맛있으니까. 흑돼지 구이는 \n멸치로 만든 젓갈인 ‘멜젓’ 에 찍어 먹으면 맛이 더욱 좋아진다. \n고기의 기름진 느끼함을 없애줌과 동시에 입맛을 돋운다. \n깨끗한 바다와 산이 둘러싸인 제주의 색다른 맛. 진정한 제주의 \n음식을 접하고 싶다면 제주의 향토음식을 그냥 지나칠 수 없다. \n편견을 버리면 제주의 참맛을 느낄 수 있다.\n향토음식\n흑돼지거리 제주시 탑동 일원-칠성골공영주차장 검색(임항로 8)┃제주산 돼지만을 사용하기로"}


created_at: 2026-06-01 04:13:05
topic: persistent://default/default/80d8d54a-809f-4b2e-89e9-7bb042a24d9e
id: acafc37f-ed52-4bff-9f6a-3f36ee10b447
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{"trapped":"/False","moddate":"2026-03-12T11:30:50+09:00","chroma:document":"약속한 고깃집들이 모여 있는 거리이다. 여기에서라면 제주도까지 와서 다른 지역 돼지고기를 먹을 걱정을 안 \n해도 될 듯!\n꿩요리                                                                                                    \n제주엔 꿩이 많아 꿩요리 종류가 다양하다. \n꿩의 가슴살을 얇게 저며 샤부샤부로 먹는 \n꿩토렴은 별미이다. 제주산 메밀과 꿩 육수가 \n어우러진 꿩메밀국수와 꿩만두국은 기름기 \n없이 깔끔한 맛을 자랑한다.\n말요리                                                                                                              \n말요리는 옛날에 왕의 수라상에 진상되었던 \n최고급 음식이었다. 마육회, 마초밥, 탕수육, \n갈비찜, 생구이, 불고기 등의 다양한 요리로 \n응용된다. 말고기는 소고기보다 담백하다.","total_pages":42,"creator":"Adobe InDesign 21.0 (Windows)","source":"./data/제주관광가이드.pdf","page":13,"page_label":"14","producer":"Adobe PDF Library 18.0","creationdate":"2026-03-12T11:22:44+09:00"}


created_at: 2026-06-01 04:13:05
topic: persistent://default/default/80d8d54a-809f-4b2e-89e9-7bb042a24d9e
id: 8a9ffc2f-8a66-4b07-8936-07b28bf5074d
encoding: FLOAT32
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{"creationdate":"2026-03-12T11:22:44+09:00","creator":"Adobe InDesign 21.0 (Windows)","page_label":"14","chroma:document":"갈비찜, 생구이, 불고기 등의 다양한 요리로 \n응용된다. 말고기는 소고기보다 담백하다.\n성게미역국\n성게를 넣고 끓인 성게미역국은 제주도 경조사에 올라오는 단골 음식이다. 순두부처럼 엉겨 \n달짝지근하면서도 담백한 맛이 난다. 제주에서는 성게를 ‘구살’이라 불러 구살국이라고도 한다.\n24JEJU   ISL AND   POCKET   BOOK —25\nCulture  Jeju","producer":"Adobe PDF Library 18.0","source":"./data/제주관광가이드.pdf","trapped":"/False","moddate":"2026-03-12T11:30:50+09:00","page":13,"total_pages":42}


created_at: 2026-06-01 04:13:05
topic: persistent://default/default/80d8d54a-809f-4b2e-89e9-7bb042a24d9e
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{"chroma:document":"빙떡\n고운 메밀가루를 반죽하여 얇고 둥글게 전을 \n부친 후 무, 당근, 파, 소금, 참깨가루, 참기름 \n등을 골고루 섞어 소를 만들고 전 위에  \n올려놓아 둥글게 말면 빙떡이 된다. \n오메기떡\n차조 반죽으로 빚는 제주의 전통 떡이다. \n통팥에 버무려진 떡 속에 달콤한 팥 앙금이 \n들어있는 것이 기본. 각종 매스컴에 알려진 \n유명 떡집은 문전성시를 이룬다. 요즘은 \n견과류나 과일 등을 응용한 다양한 맛의 \n오메기떡도 여행객을 유혹한다.\n전복죽\n해녀의 고장 제주에서 먹는 전복죽은 특별\n하다. 전복 내장까지 으깨어 끓여 푸른빛을 내는\n것이 특징이며, 어디를 가든 푸짐한 전복을\n자 랑한다. 해안가에 위치한 지역 해녀의\n집에서는 해녀들이 직접 요리한 손맛을 느낄 \n수 있다.\n보말칼국수\n고둥을 이르는 제주말인 ‘보말’. 해안가 돌 틈에서  \n깨끗하게 자란 보말을 사용해 진하고 고소한 국물 맛이 \n입맛을 돋운다. 자극적이지 않고 깔끔한 맛의 칼국수.\n생선회","page":14,"moddate":"2026-03-12T11:30:50+09:00","creationdate":"2026-03-12T11:22:44+09:00","creator":"Adobe InDesign 21.0 (Windows)","page_label":"15","trapped":"/False","source":"./data/제주관광가이드.pdf","producer":"Adobe PDF Library 18.0","total_pages":42}


created_at: 2026-06-01 04:13:05
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{"moddate":"2026-03-12T11:30:50+09:00","page":14,"creationdate":"2026-03-12T11:22:44+09:00","total_pages":42,"page_label":"15","chroma:document":"입맛을 돋운다. 자극적이지 않고 깔끔한 맛의 칼국수.\n생선회\n바다로 둘러싸인 제주에서 생선회는 흔한 음식이다. \n최고 횟감으로 불리는 다금바리부터 방어, 갈치, 고등어, \n한치까지 수많은 어종의 횟감이 즐비하다. 코스별로 \n준비해 주는 횟집 말고도 시장이나 마트 코너에서도 \n가성비 좋은 회를 즐길 수 있다.\n물회\n무더운 여름에 시원한 물회만 한 음식도 \n드물다. 자리물회, 전복물회, 한치물회 등 \n재료가 싱싱한 데다 맛도 뛰어나 기 때문에 \n취향에 따라 골라 먹기만 하면 된다.  \n제주도 물회는 된장을 베이스로 하는 것이 \n기본. 남은 양념에 공깃밥 한 공기를 슥슥 \n말아먹는 것도 맛있다.\n문게죽\n‘문게’는 ‘문어’의 제주어. 문게죽은 소박한 서민 보양음식으로, 내장을 제거하고 손질한 문어를 \n절구에 찧어서 물에 불린 쌀과 함께 참기름에 볶은 후 물을 부어 끓인 음식이다.  \n깅이죽\n‘깅이’는 ‘게’의 제주어. 5월의 별미인  \n깅이죽은 깅이를 돌절구에 찧으면 나오는","producer":"Adobe PDF Library 18.0","source":"./data/제주관광가이드.pdf","creator":"Adobe InDesign 21.0 (Windows)","trapped":"/False"}


created_at: 2026-06-01 04:13:05
topic: persistent://default/default/80d8d54a-809f-4b2e-89e9-7bb042a24d9e
id: ba4fed95-851f-4988-83ad-0e97be68b243
encoding: FLOAT32
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{"creationdate":"2026-03-12T11:22:44+09:00","moddate":"2026-03-12T11:30:50+09:00","page_label":"15","creator":"Adobe InDesign 21.0 (Windows)","producer":"Adobe PDF Library 18.0","total_pages":42,"chroma:document":"‘깅이’는 ‘게’의 제주어. 5월의 별미인  \n깅이죽은 깅이를 돌절구에 찧으면 나오는 \n진한 즙에 물을 넣고 채에 걸러낸 국물에 \n서서히 끓여낸다. \n몸국\n제주에서 흔히 나는 해초인 모자반을 이르는 말이 \n‘몸’이다. 돼지고기 육수에 모자반과 메밀을 넣어 \n걸쭉하게 끓인 것이 몸국이다. 소박하지만 든든한 \n맛을 자랑하는 제주의 별미다.\n몸국\n제주에서 흔히 나는 해초인 모자반을 이르는 말이 \n‘몸’이다. 돼지고기 육수에 모자반과 메밀을 넣어 \n걸쭉하게 끓인 것이 몸국이다. 소박하지만 든든한 \n서부두횟집거리 제주시 탑동 일원┃제주항 서부두 방파제를 따라 횟집들이 늘어서 있다. 생선회와 \n다양한 해산물 밑반찬 그리고 마지막으로 얼큰한 생선매운탕이나 생선죽을 먹을 수 있다.\n26JEJU   ISL AND   POCKET   BOOK —27\nCulture  Jeju","page":14,"source":"./data/제주관광가이드.pdf","trapped":"/False"}


created_at: 2026-06-01 04:13:05
topic: persistent://default/default/80d8d54a-809f-4b2e-89e9-7bb042a24d9e
id: ad8a435f-7e18-4a4b-9a36-856a638384a4
encoding: FLOAT32
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{"total_pages":42,"creator":"Adobe InDesign 21.0 (Windows)","producer":"Adobe PDF Library 18.0","page":15,"trapped":"/False","page_label":"16","creationdate":"2026-03-12T11:22:44+09:00","chroma:document":"수제햄버거\n간편해 보이지만 결코 가벼운 맛은 \n아닌 수제햄버거 맛집도 많다. 제주 \n로컬 재료들을 이용하거나 자기만의 \n특별한 레시피로 승부하는 맛집들. \n바다를 바라보며 먹는 햄버거는  \n제주에서 누릴 수 있는 특권일 것이다.\n지역색이 강해 향토음식이 다소 버거운 여행자라면 걱정하지 말기를. \n지금 제주는 전국 어디서도 뒤지지 않는 맛집들이 풍부한 섬이다. \n싱싱한 로컬푸드를 활용하고 참신한 아이디어까지 더한 핫한 음식들이 여기에 있다.\n두루치기\n고추장 양념된 돼지고기에 콩나물, 파채 등을 올려 볶아낸 제주 \n두루치기. 흑돼지의 고장에서 먹는 두루치기는 육지의 맛과 \n비교불가. 저렴한 가격으로 실속 있는 한 끼 식사가 가능하다.\n모닥치기\n제주에는 떡볶이 맛집도 많다. 국물이 넉넉한 \n떡볶이에 튀김과 김밥 등을 모두 버무려 담아낸  \n것을 모닥치기라고 부른다. 육지에서 흔히 말하는 \n‘김떡순’의 제주도 버전. 재래시장을 구경하고 간단히 \n모닥치기로 요기를 하는 것도 좋을 것이다.","source":"./data/제주관광가이드.pdf","moddate":"2026-03-12T11:30:50+09:00"}


created_at: 2026-06-01 04:13:05
topic: persistent://default/default/80d8d54a-809f-4b2e-89e9-7bb042a24d9e
id: e2cbe028-b73d-46a4-a31c-8074a01e1eea
encoding: FLOAT32
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{"source":"./data/제주관광가이드.pdf","producer":"Adobe PDF Library 18.0","page_label":"16","creationdate":"2026-03-12T11:22:44+09:00","chroma:document":"모닥치기로 요기를 하는 것도 좋을 것이다.\n베이커리와 디저트\nSNS를 보다 보면 제주도에서 소위 ‘인생빵’을 만났다는 후기를 \n심심찮게 볼 수 있다.  천연발효종을 이용한 빵. 제주에서 나는 \n당근을 이용한 당근케이크 등. 하나하나 열거하기 벅찰 정도로 \n많은 베이커리와 디저트 맛집이 제주도 전역에 흩어져 있다. \n오로지 디저트 맛집을 투어하는 여행객도 많다.\n제주 특산물 빵\n재래시장에서 좀녀빵, 문어빵,  \n하루방빵 등 다양한 재료와 모양의 \n앙증맞은 이색빵들이 인기다. 깜찍한 \n모양과 풍부한 맛까지. 선물로  \n안성맞춤이다.\n커피와 음료\n감성 충만한 인테리어와 제주에서만 볼 수 있는 이색적인 풍경. \n바리스타가 만들어 내는 다양한 커피의 맛을 자랑하는 수많은 감각적인 \n카페들이 여러분을 기다린다. 귤이나 한라봉을 이용한 이색적인 음료나 \n더운 여름에 이색적인 빙수까지. 바쁜 여행 일정 사이에 즐기는 꿀맛 \n휴식. 인생샷을 남길 수 있는 것은 덤이다.\n푸드트럭","trapped":"/False","page":15,"moddate":"2026-03-12T11:30:50+09:00","total_pages":42,"creator":"Adobe InDesign 21.0 (Windows)"}


created_at: 2026-06-01 04:13:05
topic: persistent://default/default/80d8d54a-809f-4b2e-89e9-7bb042a24d9e
id: e2ff332e-d0bc-405f-abf3-2d44d6bb7353
encoding: FLOAT32
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{"moddate":"2026-03-12T11:30:50+09:00","trapped":"/False","creationdate":"2026-03-12T11:22:44+09:00","source":"./data/제주관광가이드.pdf","creator":"Adobe InDesign 21.0 (Windows)","total_pages":42,"chroma:document":"휴식. 인생샷을 남길 수 있는 것은 덤이다.\n푸드트럭\n분식부터 양식까지. 푸드트럭의 변신은 무궁무진하다. 주로 간단한 \n음식을 팔던 예전과 달리 요즘은 새우요리나 스테이크까지 맛볼 수 \n있다. 주로 개인 SNS로 그날의 위치를 공지한다. 재료가 떨어지면 \n금방 마감을 하는 푸드트럭도 많으니 근처를 지난다면 서둘러 \n방문해보자.\n김밥\n김밥은 특별한 음식이 아니라고 할 분도 있겠다. 하지만 \n제주에서만 맛볼 수 있는 특별한 김밥들도 있다. 수많은 \n연예인들까지 인증한 예약을 하지 않으면 맛볼 수 없는 김밥부터 \n한 줄을 다 먹기도 버거운  엄청난 크기의  김밥, 전복 한 마리가 \n통 크게 올라간 김밥까지. 오늘 점심은 김밥도 좋겠다.\n모닥치기\n많은 베이커리와 디저트 맛집이 제주도 전역에 흩어져 있다. \n오로지 디저트 맛집을 투어하는 여행객도 많다.\n일반음식\n다양한 음식과 맛집 정보는 비짓제주(www.visitjeju.net)에서 확인할 수 있다.","page":15,"producer":"Adobe PDF Library 18.0","page_label":"16"}


created_at: 2026-06-01 04:13:05
topic: persistent://default/default/80d8d54a-809f-4b2e-89e9-7bb042a24d9e
id: 5e6d2856-16b4-446b-943e-cde337bddf11
encoding: FLOAT32
metadata:
{"moddate":"2026-03-12T11:30:50+09:00","creator":"Adobe InDesign 21.0 (Windows)","page":15,"producer":"Adobe PDF Library 18.0","source":"./data/제주관광가이드.pdf","creationdate":"2026-03-12T11:22:44+09:00","trapped":"/False","total_pages":42,"page_label":"16","chroma:document":"28JEJU   ISL AND   POCKET   BOOK —29\nCulture  Jeju"}


created_at: 2026-06-01 04:13:05
topic: persistent://default/default/80d8d54a-809f-4b2e-89e9-7bb042a24d9e
id: 766cee0e-f2df-42eb-ad35-554a6bc868bb
encoding: FLOAT32
metadata:
{"creator":"Adobe InDesign 21.0 (Windows)","page_label":"17","total_pages":42,"producer":"Adobe PDF Library 18.0","source":"./data/제주관광가이드.pdf","trapped":"/False","creationdate":"2026-03-12T11:22:44+09:00","chroma:document":"게스트하우스\n여행자들의 여행 패턴이 변화하며 게스트하우스 역시 \n변화를 꾀하고 있다. 도미토리 위주의 단체실에서 \n벗어나 1인실과 2인실로 변화하는 추세이며. 한정된 \n공간 안에서 최대한 프라이빗 한 공간을 제공하려 \n노력하는 모양새다. 조식을 제공 등 이른바 호텔식 \n서비스를 누릴 수 있는 가성비 숙소로 변화하고 있다.\n풀빌라\n여행의 피로를 풀기에 안성맞춤인 스파와 개인 풀장, \n노천탕까지 겸비한 럭셔리 펜션들이 많이 들어서 있다. \n높은 가격대의 숙박비를 자랑하는 곳도 있지만 특별한 \n순간을 기념하기 위한 여행자들에게는 좋은 선택지가 될 \n것이다.\n글램핑\n제주에서 즐기는 캠핑. 캠핑에 관심이 있는 여행객이라면 \n솔깃한 제안일 것이다. 무거운 장비를 챙기지 않고도 가볍게 \n캠핑을 즐길 수 있는 장소가 있다. 숙박에 필요한 모든 것이 \n갖춰진 캠핑카에서 하룻밤을 보내거나 침대까지 갖춰진 텐트로 \n무장한 글램핑장에서의 하룻밤도 좋은 추억이 될 것이다.\n안심숙소","moddate":"2026-03-12T11:30:50+09:00","page":16}


created_at: 2026-06-01 04:13:05
topic: persistent://default/default/80d8d54a-809f-4b2e-89e9-7bb042a24d9e
id: 26628d94-6046-4998-8167-0845d562cb56
encoding: FLOAT32
metadata:
{"page":16,"producer":"Adobe PDF Library 18.0","total_pages":42,"source":"./data/제주관광가이드.pdf","creator":"Adobe InDesign 21.0 (Windows)","moddate":"2026-03-12T11:30:50+09:00","chroma:document":"무장한 글램핑장에서의 하룻밤도 좋은 추억이 될 것이다.\n안심숙소\n‘농어촌민박 안전인증제’는 제주도가 \n전국 최초로 시행하는 제도로서 최근 \n급속히 증가하고 있는 민박업소에 \n대하여 안전하고 청결한 민박  \n환경을 조성하고 이용객들의 안전과 \n서비스 향상은 물론 민박 경쟁력을 \n향상시키기 위해 마련됐다.\n \n더 많은 숙소 정보 비짓제주(www.visitjeju.net)\n제주도 숙소 소개\n제주도에는 호텔부터 게스트하우스까지 다양한 종류의 숙소가 있다. 가족, 연인을 위한 아늑하고 \n낭만적인 호텔이나 친구와 여행하기에 좀 더 편한 펜션이 있고 대가족은 독립적인 공간의 독채 \n펜션\n  \n혹은 리조트가 좋을 것이다. 부모님을 모시고 가거나 아이와 함께 간다면 수영장이 있는 \n호텔이나 풀빌라를 선택하는 것이 좋다.\n호텔 / 리조트\n관광도시 제주에는 수많은 호텔들이 있다. 특급호텔은 \n말할 것도 없고 실속 여행자를 위한 비즈니스급 호텔까지.","page_label":"17","creationdate":"2026-03-12T11:22:44+09:00","trapped":"/False"}


created_at: 2026-06-01 04:13:05
topic: persistent://default/default/80d8d54a-809f-4b2e-89e9-7bb042a24d9e
id: 73d4e526-d9bf-4d41-be0e-560cf4592ca8
encoding: FLOAT32
metadata:
{"total_pages":42,"source":"./data/제주관광가이드.pdf","creationdate":"2026-03-12T11:22:44+09:00","creator":"Adobe InDesign 21.0 (Windows)","moddate":"2026-03-12T11:30:50+09:00","producer":"Adobe PDF Library 18.0","page":16,"page_label":"17","trapped":"/False","chroma:document":"말할 것도 없고 실속 여행자를 위한 비즈니스급 호텔까지. \n여행자의 편의에 따라 선택의 폭이 무척 넓다. 할인 사이트를 \n이용하거나 특가 할인을 받는다면 호텔은 제주를 여행하는 데 \n있어 가장 저렴하고 편안한 숙박시설 중에 하나일 것이다.\n독채펜션\n집 한 채를 온전히 대여해 주는 숙소의 형태이다. 한 공간을 \n오롯이 누릴 수 있는 것이 장점이며 주로 오래된 제주 \n가옥을 리모델링하여 돌담과 마당 등 제주의 분위기를 \n온전히 느낄 수 있는 숙소가 많다. 일반 숙소에 비해 \n가격대는 높은 편이지만 다른 사람에게 방해받지 않길 \n원하는 여행객들에게 내 집 같은 편안함을 제공할 것이다.\n30\nJEJU   ISL AND   POCKET   BOOK— 31\nCulture  Jeju"}


created_at: 2026-06-01 04:13:05
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{"creationdate":"2026-03-12T11:22:44+09:00","producer":"Adobe PDF Library 18.0","trapped":"/False","source":"./data/제주관광가이드.pdf","creator":"Adobe InDesign 21.0 (Windows)","chroma:document":"만장굴\n 제주시 구좌읍 만장굴길 182\n화산 용암의 침하 운동으로 생성된 세계 최고 규모의 용암 동굴. \n궁전에 들어온 듯한 내부의 경관은 웅장함이 느껴지는데, 제주도를 \n닮은 정교한 돌거북은 관광객들에게 인기가 있다.\n(2026년 개관)\n비자림\n 제주시 구좌읍 비자숲길 55\n약 45만㎡ 규모에 500~800년생 비자나무 2,800여 그루가 \n군락을 이루고 있는 곳. 단순림으로는 대한민국 최대 규모로 \n천연기념물이다. 하늘이 보이지 않을 정도로 빽빽하게 우거진 \n숲길을 걷다 보면 마음속까지 절로 시원해진다.\n제주관광 추천 30\n거문오름\n 제주시 조천읍 선교로 569-36\n제주도의 오름 중 유일하게 유네스코 세계자연유산에 등재 \n되었는데, 화산지형과 용암동굴은 지질학적 가치가 매우 높다. \n트레킹은 예약제로 운영되며, 당일예약은 불가하다.\n렛츠런파크\n 제주시 애월읍 평화로 2144\n경마장으로 매주 금요일과 토요일에 제주마 경주가 열린다. 말 테마","moddate":"2026-03-12T11:30:50+09:00","page_label":"18","page":17,"total_pages":42}


created_at: 2026-06-01 04:13:05
topic: persistent://default/default/80d8d54a-809f-4b2e-89e9-7bb042a24d9e
id: 1bff6720-f5fa-42a4-adfc-347934a3e423
encoding: FLOAT32
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{"moddate":"2026-03-12T11:30:50+09:00","creationdate":"2026-03-12T11:22:44+09:00","page_label":"18","chroma:document":"경마장으로 매주 금요일과 토요일에 제주마 경주가 열린다. 말 테마\n파크로서 조랑말 승마체험, 무료 파크골프장, 미니 조랑말 박물관, \n어린이 놀이시설이 있다. 경마를 즐기지 않는 관광객도 방문하기에 \n좋다.\n내가 본 제주는 어디까지일까?\n크고 작은 오름과 숲길. 어디에서나 마주치는 한라산, 푸른 바다와 개성 만점의 관광지까지.\n제주의 보석 같은 여행지가 여러분의 선택을 기다린다.\n※ 비짓제주 연간 조회수 참조, 가나다 순\n32\nJEJU   ISL AND   POCKET   BOOK— 33\nTravel  Jeju","producer":"Adobe PDF Library 18.0","page":17,"trapped":"/False","creator":"Adobe InDesign 21.0 (Windows)","total_pages":42,"source":"./data/제주관광가이드.pdf"}


created_at: 2026-06-01 04:13:05
topic: persistent://default/default/80d8d54a-809f-4b2e-89e9-7bb042a24d9e
id: 65b64578-f8a5-4f4a-a3f2-c02ac7154017
encoding: FLOAT32
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{"page_label":"19","moddate":"2026-03-12T11:30:50+09:00","page":18,"trapped":"/False","chroma:document":"아침미소목장\n 제주시 첨단동길 160-20\n제주시 중산간에 위치한 목장으로, 유기농 우유로 만든 유제품을 \n만나 볼 수 있고, 너른 초지에서는 사진 찍기가 좋다. 동물과 \n교감하고, 체험도 할 수 있다. 체험은 유료이며, 예약을 해야 한다.\n쇠소깍\n 서귀포시 쇠소깍로 104\n제주 올레 6코스의 시작 지점으로 바닷물과 민물이 만나는 \n비밀스러운 계곡. 양벽에 기암괴석이 병풍처럼 둘러서 있고, 그 위로 \n숲이 우거져 신비한 계곡에 온 기분을 느낄 수 있다.\n에코랜드 테마파크\n 제주시 조천읍 남조로 1982\n기차를 타고 약 4.5km 거리의 곶자왈 원시림을 체험하는 테마파크. \n봄과 여름에는 푸른 녹음과 따뜻한 햇살을, 가을에는 알록달록 \n피어난 코스모스와 억새 물결을, 그리고 겨울에는 새하얀 설경을 \n볼 수 있다.\n애월한담해안산책로\n 제주시 애월읍 애월리 2461-1\n총 길이 1.2km의 해안 산책로이며, 바로 옆에 파도가 참방거리는","producer":"Adobe PDF Library 18.0","total_pages":42,"creationdate":"2026-03-12T11:22:44+09:00","source":"./data/제주관광가이드.pdf","creator":"Adobe InDesign 21.0 (Windows)"}


created_at: 2026-06-01 04:13:05
topic: persistent://default/default/80d8d54a-809f-4b2e-89e9-7bb042a24d9e
id: 10f36834-01ec-4cd7-bdfb-bb286f276520
encoding: FLOAT32
metadata:
{"creationdate":"2026-03-12T11:22:44+09:00","producer":"Adobe PDF Library 18.0","page_label":"19","creator":"Adobe InDesign 21.0 (Windows)","page":18,"chroma:document":"총 길이 1.2km의 해안 산책로이며, 바로 옆에 파도가 참방거리는 \n해안 길을 따라 걸을 수 있다.  바위로 이루어진 해안을 따라 \n구불구불하게 이어지는 산책길은 일몰 감상 포인트이기도 하다.\n오설록티뮤지엄\n 서귀포시 안덕면 신화역사로 15\n국내외 차 관련 물품과 박물관이며, 푸른 녹차밭이 펼쳐져 있는 \n풍경으로 유명. 티 하우스에서는 티 마스터가 직접 볶은 따뜻한 차와 \n오설록 녹차로 만든 음료 아이스크림, 롤케이크 등을 즐길 수 있다\n성산일출봉\n 서귀포시 성산읍 일출로 284-6\n바다 한가운데서 화산이 수중 폭발해 생겨난 화구. 보존가치가 \n높아 천연기념물로 지정. 2007년 UNESCO 세계자연유산에 등재, \n2010년에는 UNESCO 세계지질공원으로 인증되었다.\n섭지코지\n 서귀포시 성산읍 고성리 62-3\n드라마 촬영지로 유명한 곳. 봄에는 노란 유채꽃과 성산일출봉을 \n배경으로 한 해안절경이 장관이고, 가을에는 잔디에 돋아난","source":"./data/제주관광가이드.pdf","trapped":"/False","moddate":"2026-03-12T11:30:50+09:00","total_pages":42}


created_at: 2026-06-01 04:13:05
topic: persistent://default/default/80d8d54a-809f-4b2e-89e9-7bb042a24d9e
id: 20ebfeb4-7281-4d77-aced-5017e36cfa24
encoding: FLOAT32
metadata:
{"moddate":"2026-03-12T11:30:50+09:00","producer":"Adobe PDF Library 18.0","creator":"Adobe InDesign 21.0 (Windows)","chroma:document":"배경으로 한 해안절경이 장관이고, 가을에는 잔디에 돋아난 \n들꽃들과 은빛 물결의 억새가 로맨틱한 분위기를 자아낸다.\n사려니숲길\n 제주시 조천읍 교래리 산 137\n사려니숲길은 제주시의 숨은 비경 31곳 중 하나. 비자림로를 \n시작으로 물찻오름과 사려니 오름을 거쳐가는 삼나무가 우거진 \n숲길이다. 숲의 모습이 훼손되지 않아 트래킹을 좋아하는  \n여행자들이 즐겨 찾는다.\n새별오름\n 제주시 애월읍 봉성리 산59-8\n제주 서부 오름 들 중 으뜸가는 대표적인 오름. 경사가 약간 있으나 \n높이는 해발 519.3m(정상까지 소요시간은 30분 내외)로 그리 높지 \n않다. 매년 3월 경 제주도를 대표하는 축제인 들불축제가 열린다. \n왕따 나무는 포토스팟으로 유명하다.\n산굼부리\n 제주시 조천읍 교래리 산38\n제주도의 여느 기생화산과 달리 밑에서 폭발하여 폭발물이 쌓이지 \n않고 뻥 뚫린 분화구로 형성된 폭렬공 기생화산. 세계적으로도 \n희귀하다. 난대와 온대성 식물이 함께 있는 거대한 자연식물원이다.","page":18,"page_label":"19","creationdate":"2026-03-12T11:22:44+09:00","source":"./data/제주관광가이드.pdf","total_pages":42,"trapped":"/False"}


created_at: 2026-06-01 04:13:05
topic: persistent://default/default/80d8d54a-809f-4b2e-89e9-7bb042a24d9e
id: e09bfe14-016a-4277-8b62-e54085e95d21
encoding: FLOAT32
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{"source":"./data/제주관광가이드.pdf","creator":"Adobe InDesign 21.0 (Windows)","creationdate":"2026-03-12T11:22:44+09:00","chroma:document":"희귀하다. 난대와 온대성 식물이 함께 있는 거대한 자연식물원이다.\n34\nJEJU   ISL AND   POCKET   BOOK— 35\nTravel  Jeju","page_label":"19","moddate":"2026-03-12T11:30:50+09:00","trapped":"/False","producer":"Adobe PDF Library 18.0","total_pages":42,"page":18}


created_at: 2026-06-01 04:13:05
topic: persistent://default/default/80d8d54a-809f-4b2e-89e9-7bb042a24d9e
id: ac0d7669-5aa9-4344-bb35-6cd1fb9f1858
encoding: FLOAT32
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{"chroma:document":"주상절리대(중문대포해안)\n 서귀포시 중문동 2763\n대한민국 최대 규모이자 천연기념물인 이곳의 주상절리는 용암이 \n화구로부터 흘러나와 급격히 식으면서 발생한 수축 작용의 \n결과물이다. 계단을 쌓은 듯 겹겹이 병풍처럼 서 있는 돌기둥이 \n자연의 신비를 보여준다.\n제주절물자연휴양림\n 제주시 명림로 584\n청정한 공기와 숲이 주는 힐링감이 좋아 많은 관광객이 즐겨찾는 \n제주시 명소다. 안개가 끼면 몽환적인 분위기를 연출해서 비가 \n오는 날씨에도 찾는 관광객이 많다. 산책로는 경사가 낮고 완만해서 \n어린이, 노약자, 장애인도 이용하기 편리하다.\n천지연폭포\n 서귀포시 남성중로 2-9\n천지연은 하늘과 땅이 만나 이루어진 연못이라는 의미를 담고 \n있는데, 폭포의 길이 22m, 그 아래 못의 깊이가 20m이다. 오후 \n10시까지 야간개장을 하는데, 밤에 보는 폭포의 모습도 장관이다.\n천제연폭포\n 서귀포시 천제연로 132\n칠선녀가 목욕을 하다 간다는 전설이 있는 폭포. 천제연폭포는","total_pages":42,"moddate":"2026-03-12T11:30:50+09:00","creator":"Adobe InDesign 21.0 (Windows)","page_label":"20","creationdate":"2026-03-12T11:22:44+09:00","trapped":"/False","page":19,"producer":"Adobe PDF Library 18.0","source":"./data/제주관광가이드.pdf"}


created_at: 2026-06-01 04:13:05
topic: persistent://default/default/80d8d54a-809f-4b2e-89e9-7bb042a24d9e
id: e83f10d8-5826-4cc8-a1d8-bbab32ee0d7c
encoding: FLOAT32
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{"page_label":"20","producer":"Adobe PDF Library 18.0","total_pages":42,"page":19,"source":"./data/제주관광가이드.pdf","creationdate":"2026-03-12T11:22:44+09:00","creator":"Adobe InDesign 21.0 (Windows)","moddate":"2026-03-12T11:30:50+09:00","chroma:document":"서귀포시 천제연로 132\n칠선녀가 목욕을 하다 간다는 전설이 있는 폭포. 천제연폭포는 \n3개의 폭포로 나누어지는데, 칠선녀다리로 불리는 선임교와 \n난대림으로 산책하기에도 좋다.\n카멜리아힐\n 서귀포시 안덕면 병악로 166\n사계절마다 다양한 풍경이 있는, 제주의 자연을 담은 동백 \n수목원으로, 80개국의 동백나무 500여 종 6,000여 그루가 울창한 \n숲을 이루고 있다. 다양한 식물들이 예쁜 풍경을 이루고 있어 사진 \n찍기 좋은 곳.\n월정리해변\n 제주시 구좌읍 월정리 33-3\n달이 머문다'는 뜻의 이름을 가진 서정적인 풍경의 해변. 서핑, \n스노클링, 카약 등 다양한 수상 레포츠의 즐거움을 만끽하는 \n사람들도 있다. 아름다운 풍경과 시원한 바다 내음을 맡으며 쉴 수 \n있는 아름다운 해변.\n우도\n 서귀포시 성산읍 성산등용로 112-7(우도도항선선착장)\n소가 누워 있는 모습과 닮아 있다는 우도. 푸른 제주바다와 아늑한 \n풀밭, 하얀 백사장 풍경이 무척 인상적이다. 특히 별빛이 쏟아져","trapped":"/False"}


created_at: 2026-06-01 04:13:05
topic: persistent://default/default/80d8d54a-809f-4b2e-89e9-7bb042a24d9e
id: 822b532e-186f-4d0a-b148-7eceafef4275
encoding: FLOAT32
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{"moddate":"2026-03-12T11:30:50+09:00","total_pages":42,"trapped":"/False","creationdate":"2026-03-12T11:22:44+09:00","source":"./data/제주관광가이드.pdf","creator":"Adobe InDesign 21.0 (Windows)","producer":"Adobe PDF Library 18.0","page_label":"20","chroma:document":"풀밭, 하얀 백사장 풍경이 무척 인상적이다. 특히 별빛이 쏟아져 \n내리는 우도의 밤 풍경은 낮에는 볼 수 없는 또 다른 아름다움을 \n선사한다.\n제주아쿠아플라넷\n 서귀포시 성산읍 섭지코지로 95\n아시아 최대 규모(연면적 2만5600m², 1만800톤)를 자랑하는 \n아쿠아리움으로 이는 63 씨월드의 약 11배에 달하는 규모로, 단일 \n수조로는 세계 최대급이다. 500여종 4만8000마리의 전시생물 \n또한 세계 top10안에 든다. \n정방폭포\n 서귀포시 칠십리로214번길 35\n풍광이 뛰어난 제주의 3대 폭포 중 하나로, 폭포수가 바다로 \n떨어지는 동양 유일의 해안폭포. 자연과 조화롭게 어울리며 \n단정하게 떨어지는 모습이 전통 수묵화를 감상하는 느낌을 준다.\n용머리해안\n 서귀포시 안덕면 사계남로216번길 24-30 \n바다속으로 들어가는 용의 머리를 닮았다 해서 용머리해안으로 \n불린다. 수천만년 동안 층층이 쌓인 사암층 암벽이 파도에 깎여","page":19}


created_at: 2026-06-01 04:13:05
topic: persistent://default/default/80d8d54a-809f-4b2e-89e9-7bb042a24d9e
id: ebd5f025-7d9b-4821-898c-b22c627a4bd6
encoding: FLOAT32
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{"producer":"Adobe PDF Library 18.0","total_pages":42,"moddate":"2026-03-12T11:30:50+09:00","trapped":"/False","page_label":"20","chroma:document":"불린다. 수천만년 동안 층층이 쌓인 사암층 암벽이 파도에 깎여 \n기묘한 절벽을 이루고 있다. 한바퀴 돌아보는데 30분 정도가 \n소요되는데, 만조와 기상악화로 출입통제가 될 수 있으니, 관람당일 \n미리 확인(064-794-2940)이 필요하다.\n36\nJEJU   ISL AND   POCKET   BOOK— 37\nTravel  Jeju","source":"./data/제주관광가이드.pdf","page":19,"creationdate":"2026-03-12T11:22:44+09:00","creator":"Adobe InDesign 21.0 (Windows)"}


created_at: 2026-06-01 04:13:05
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{"total_pages":42,"page_label":"21","creator":"Adobe InDesign 21.0 (Windows)","creationdate":"2026-03-12T11:22:44+09:00","chroma:document":"황우지해안\n 서귀포시 서홍동 795-2\n서귀포의 대표적인 관광명소인 외돌개에서 도보로 5분이면 닿는 \n거리에 있다. 암석 아래쪽으로 바닷물이 순환되면서 맑은 물이 \n유지되어 물놀이를 즐기기 좋다. 스노클링 하기 좋은 장소로 알려져 \n많은 사람이 찾는다.\n(낙석으로 인해 폐쇄 중)\n협재해수욕장\n 제주시 한림읍 협재리 2497-1\n제주 서쪽 해수욕장 중 1, 2위에 꼽히는 해수욕장. 어린왕자 동화 속 \n보아 뱀 형태의 비양도와 에메랄드빛 바다와 하얀 백사장 풍경이 \n아름답다. 경사가 완만해 수심이 낮아 어린아이와 해수욕을 즐기기 \n좋다.\n휴애리\n 서귀포시 남원읍 신례동로 256 \n다양하고 예쁜 포토존들이 구성되어 있고, 매시간 정각에 공연하는 \n인기 절정 흑돼지야 놀자 공연, 계절별 축제 외에도 승마체험, \n동물교감 체험 등 다양한 체험 프로그램이 준비되어 있어 커플이나 \n가족단위 관광객들에게 추천하는 관광지. \n한라산국립공원\n 제주시 조천읍 516로 1865(성판악)","producer":"Adobe PDF Library 18.0","source":"./data/제주관광가이드.pdf","page":20,"moddate":"2026-03-12T11:30:50+09:00","trapped":"/False"}


created_at: 2026-06-01 04:13:05
topic: persistent://default/default/80d8d54a-809f-4b2e-89e9-7bb042a24d9e
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{"trapped":"/False","moddate":"2026-03-12T11:30:50+09:00","source":"./data/제주관광가이드.pdf","chroma:document":"한라산국립공원\n 제주시 조천읍 516로 1865(성판악)\n높이 1,950미터로 남한에서 가장 높다. 화산활동으로 생성된 \n산으로 UNESCO 생물권 보전지역으로 지정되었다. 등반코스는 \n총 5개 코스로, 정상까지는 성판악과 관음사 코스로만 등반이 \n가능하다.\n함덕해수욕장\n 제주시 조천읍 조함해안로 525\n키 큰 야자수들, 하얀 모래와 대조를 이루는 에메랄드빛 바다로 \n유명하다. 물이 맑고 수심이 얕아서 가족단위 피서지로도 좋으며, \n바로 옆에 자리한 서우봉에 오르면 함덕해수욕장을 한눈에 담을 수 \n있는 호사를 누릴 수 있다.\n한림공원\n 제주시 한림읍 한림로 300\n협재굴과 쌍용굴, 제주 전통 초가를 구경할 수 있는 곳. 사파리 \n조류공원에서는 원앙과 공작새, 앵무새, 꿩 등을 수석관에서는 \n제주도 특유의 수석을 볼 수 있다. 연꽃 과 수련 등이 가득한 연못 \n정원과 아열대 식물원도 있다.\n다양한 관광지 정보 확인 비짓제주(www.visitjeju.net)\n38","page_label":"21","page":20,"total_pages":42,"creationdate":"2026-03-12T11:22:44+09:00","producer":"Adobe PDF Library 18.0","creator":"Adobe InDesign 21.0 (Windows)"}


created_at: 2026-06-01 04:13:05
topic: persistent://default/default/80d8d54a-809f-4b2e-89e9-7bb042a24d9e
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created_at: 2026-06-01 04:13:05
topic: persistent://default/default/80d8d54a-809f-4b2e-89e9-7bb042a24d9e
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{"trapped":"/False","source":"./data/제주관광가이드.pdf","chroma:document":"제주국제공항에서 5분 거리, 전농로 벚꽃길              \n전농로는 제주에서 벚꽃을 감상할 수 있는 명소로 \n오랫동안 사랑받아온 곳이다.  봄이면 제주를 대표하는 \n봄꽃인 왕벚꽃이 터널을 이뤄 이 일대가 핑크빛으로 \n찬란하게 물든다. 매년 4월이면 이 일대에서 왕벚꽃 \n축제가 펼쳐지는데 다양한 먹거리 장터와 플리마켓이 \n열려 하늘에는 벚꽃 물결이, 땅에는 사람 물결이 \n일렁인다. 제주국제공항에서 차로 5분 안팎 거리에  \n위치하고 있어 오가다 쉽게 들를 수 있다. \n가파도, 초록빛 물결이 넘실넘실, 춤추는 섬\n제주의 섬 중 네 번째로 큰 섬, 가파도에는 4월이면 청보리가 섬을 온통 가득 메우고, 청보리 \n꽃 향기로 넘실댄다. 가파도 하면 청보리를 연상케 할 정도로 이제 가파도는 청보리밭의 대 명사가 \n됐다. 실제 섬의 70%가 보리밭이다. 특히 가파도의 청보리는 자라는 속도가 다른 지 역보다 2배","producer":"Adobe PDF Library 18.0","creator":"Adobe InDesign 21.0 (Windows)","moddate":"2026-03-12T11:30:50+09:00","creationdate":"2026-03-12T11:22:44+09:00","page":21,"page_label":"22","total_pages":42}


created_at: 2026-06-01 04:13:05
topic: persistent://default/default/80d8d54a-809f-4b2e-89e9-7bb042a24d9e
id: 1504cd2d-ad14-4884-a58e-ddbfee0a6c43
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{"moddate":"2026-03-12T11:30:50+09:00","total_pages":42,"producer":"Adobe PDF Library 18.0","page":21,"chroma:document":"이상 빨라 국내에서도 가장 먼저 높고 푸르게 자란다. 어디가 시작이고, 어디가 끝인지 모를 정도로 \n펼쳐진 푸른 물결을 바라보면 몸과 마음은 절로 힐링이 된다.\n사계절 아름다운 제주\n제주의 계절 관광지\n환상적인 유채꽃과 벚꽃의 조화, 녹산로 \n유채꽃 따로, 벚꽃 따로 구경하러 갈 필요 없는 곳, 바로 가시리의 녹산로다. 가시리 조랑 말체험공원 \n근처에서 시작해 약 4~5km 걸쳐 벚꽃과 환상적인 콜라보의 유채꽃 길이 아름답게 펼쳐진다. \n정석항공로라고도 불리는 이곳은 “한국의 아름다운 길 100선”으로 소개되기도 했다\n제주의 봄 만끽하기\n포근한 햇빛과 선선한 바람. 천천히 올레길을 걷기에 좋은 계절이다. \n유채꽃을 시작으로 벚꽃, 초록빛 청보리까지 각양각색의 생명이 움트는 시기다.봄\n전농로벚꽃길\n유수암리벚꽃길\n신례리왕벚나무자생지\n녹산로\n산방산\n40JEJU   ISL AND   POCKET   BOOK —41\nTravel  Jeju","creationdate":"2026-03-12T11:22:44+09:00","page_label":"22","trapped":"/False","creator":"Adobe InDesign 21.0 (Windows)","source":"./data/제주관광가이드.pdf"}


created_at: 2026-06-01 04:13:05
topic: persistent://default/default/80d8d54a-809f-4b2e-89e9-7bb042a24d9e
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{"page":22,"total_pages":42,"page_label":"23","source":"./data/제주관광가이드.pdf","creationdate":"2026-03-12T11:22:44+09:00","trapped":"/False","producer":"Adobe PDF Library 18.0","creator":"Adobe InDesign 21.0 (Windows)","chroma:document":"바다에서 즐기는 힐링, 스노클링\n간단한 장비를 갖추고 바다에서 즐기는  \n스노클링. 한번 경험해 본 사람이면 그 맛을 \n잊지 못한다. 바다로 둘러싸인 제주에는 \n판포포구 등 여행객들이 즐겨 찾는 스노클링 \n명소가 많다. 잔잔한 물결에 몸을 맡기고 \n투명한 바다를 즐겨보자. \n개성 뚜렷한 12개의 제주 바다, 제주의 해수욕장\n모두 저마다의 개성이 있는 제주의 해수욕장. 잔잔한 바다와 풍경이 아름다운 협재, 금능해수욕장. \n바다와 함께 용천수를 즐길 수 있는 곽지해수욕장, 서우봉과 어우러진 아름다운 해안 함덕해수욕장 \n등 모두 열거하기 힘들 만큼 매력적인 해변의 천국이다. 협재, 함덕, 삼양, 월정, 이호테우해수욕장 등 \n야간개장하는 해수욕장도 있다. 지도를 펴고 가장 가까운 해수욕장으로 가보자.\n얼음처럼 차가운 물줄기, 돈내코 계곡과 용천수\n돈내코 계곡은 한라산에서 내려오는 맑고 차가운 물줄기와 뛰어난 주의 경관으로 여름 피서지로","moddate":"2026-03-12T11:30:50+09:00"}


created_at: 2026-06-01 04:13:05
topic: persistent://default/default/80d8d54a-809f-4b2e-89e9-7bb042a24d9e
id: 86e379e7-ec89-4ae3-8ae8-bf5807edcd0a
encoding: FLOAT32
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{"producer":"Adobe PDF Library 18.0","total_pages":42,"creator":"Adobe InDesign 21.0 (Windows)","moddate":"2026-03-12T11:30:50+09:00","chroma:document":"유명하다. 땅으로 스며든 물이 해안가를 따라 솟아나는 용천수를 이용한 물놀이도 제주에서만 즐길 \n수 있는 이색 아이템. 도내 전 지역에 걸쳐 600여 개의 용천수가 나오는데 100여 개는 해안가에 \n위치하고 있다. 용천수를 이용한 노천욕은 제주에서만 경험할 수 있는 진귀한 체험이다.\n제주의 여름 즐기기\n깨끗한 바다와 시원한 용천수가 풍부한 제주는 자연경관도 즐기면서 물놀이를 \n하기에 좋은 곳이다. 바다는 에메랄드 빛으로 유혹하고, 한여름에도 17도를 유지하는 \n용천수는 신기하기만 하다.\n여름\n사계절 아름다운 제주   제주의 계절 관광지\n삼양해수욕장\n함덕해수욕장\n이호테우해수욕장\n협재/금능해수욕장\n중문색달해수욕장\n신양섭지해수욕장\n표선해수욕장\n화순금모래해수욕장\n곽지해수욕장\n김녕해수욕장\n월정해수욕장\n42JEJU   ISL AND   POCKET   BOOK —43\nTravel  Jeju","trapped":"/False","page":22,"source":"./data/제주관광가이드.pdf","page_label":"23","creationdate":"2026-03-12T11:22:44+09:00"}


created_at: 2026-06-01 04:13:05
topic: persistent://default/default/80d8d54a-809f-4b2e-89e9-7bb042a24d9e
id: 003e5f15-017d-4bc0-9aa5-dc2eef7f8d23
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{"source":"./data/제주관광가이드.pdf","creationdate":"2026-03-12T11:22:44+09:00","page":23,"moddate":"2026-03-12T11:30:50+09:00","producer":"Adobe PDF Library 18.0","total_pages":42,"creator":"Adobe InDesign 21.0 (Windows)","page_label":"24","chroma:document":"아름다운 곡선의 미학,  금(악)오름\n비교적 평탄한 지형에 오롯이 서있는 모습이 꽤나 고매하게 느껴지는 오름이다. 분화구를 중심으로 \n남북으로 높은 두개의 봉우리가 있다. 산정화구호가 있는 몇 안되는 오름 중 하나이다. 화구호에는 \n원래 수량이 풍부하였는데, 현재는 바닥이 드러나 있고 비가오면 물이 고이지만 오래가지는 않는다. \n이 화구호는 왕매라 불린다.산정화구호 뿐만 아니라 정상에서 바라보는 경치와 정상 분화구의 \n능선이 아름다워서 관광객들이 즐겨 찾는 오름 중 하나이다.\n오라동 메밀꽃밭\n끝이 보이지 않을 만큼 너른 부지에  \n메밀꽃이 포슬포슬 내려앉는다. 북쪽으로는 \n제주바다가 아련히 보이고 남쪽으로는 \n병풍같이 한라산이 서있어 풍경이  \n아름답기로는 더할 나위가 없다. 시즌별로 \n청보리 축제와 메밀꽃 축제를 진행해  \n왔으며, 아름다운 자연을 배경으로 사진을 \n찍기 위해 많은 관광객이 발걸음 하고 있다.\n제주 대표 억새 군락지,  따라비오름","trapped":"/False"}


created_at: 2026-06-01 04:13:05
topic: persistent://default/default/80d8d54a-809f-4b2e-89e9-7bb042a24d9e
id: 5881a7f7-2f4c-4647-8a3a-86f43ba9d6fc
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{"source":"./data/제주관광가이드.pdf","page":23,"moddate":"2026-03-12T11:30:50+09:00","creator":"Adobe InDesign 21.0 (Windows)","creationdate":"2026-03-12T11:22:44+09:00","producer":"Adobe PDF Library 18.0","page_label":"24","total_pages":42,"trapped":"/False","chroma:document":"찍기 위해 많은 관광객이 발걸음 하고 있다.\n제주 대표 억새 군락지,  따라비오름\n가을철 동부 지역을 대표하는 억새 여행지 가운데 하나이다. 길섶에 빼곡하게 피어난 반짝이는 \n억새들이 마치 스포트라이트를 비춘 듯 화려한 꽃길을 이룬다. 정상에 닿으면 또 다른 풍경이 \n기다리고 있다. 너른 들판과 병풍처럼 둘러쳐진 수많은 오름 군락이 지평선을 따라 한 폭의 그림처럼 \n걸려 있다. 따라비 오름은 분화구를 3개나 품고 있는데 이들이 서로 중첩된 풍경이 오묘하고 \n신비로운 분위기를 더한다.\n제주의 가을 속으로\n선선한 바람과 함께 찾아온 제주의 가을. 곳곳에 억새가 물결을 이루고 메밀밭은 \n소박한 매력을 뽐낸다. 시원한 바람을 맞으며 오름에 오르기도 더없이 좋은 계절이다.가을\n사계절 아름다운 제주   제주의 계절 관광지\n금백조로\n닭머르\n다랑쉬오름\n따라비오름새별오름\n오라동메밀밭\n금(악오름\n44JEJU   ISL AND   POCKET   BOOK —45\nTravel  Jeju"}


created_at: 2026-06-01 04:13:05
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{"page_label":"25","moddate":"2026-03-12T11:30:50+09:00","creationdate":"2026-03-12T11:22:44+09:00","chroma:document":"겨울 한라산의 속살, 어리목 코스\n한라산은 제주에 속해있지만, 제주와는 다른 혼자만의 날씨를 누리며 살아간다. 청명한 제주시나 \n서귀포시와는 다르게 갑자 기 비가 오거나 구름 속에 잠기는 등 늘 날씨가 제멋대로다.  얼음과 \n눈 속에 숨어버린 한라산을 찾아가려면 성판악 코스나 영실 코스가 제격이지만 어리목 코스는 \n그보다는 조금 수월하게 한라산과 밀당하며 겨울을 즐길 수 있는 눈꽃 트레킹 코스다. \n깊은 겨울 더욱 아름다운 동백꽃\n여러 종류의 꽃과 식물들이 사계절 만발한 \n곳 이지만 겨울이 깊어지면 피어나는 \n각양각색의 동백꽃의 매력이 특별하다. 바닥 \n가득 흩뿌려진 동백꽃 길을 걸으면 제주 \n겨울의 낭만에 빠져보자. 사랑하는 사람과 \n함께 꽃길 사이에서 인생샷을 남겨보자.\n영실코스\n휴애리\n어리목코스\n카멜리아힐\n관음사코스\n성판악코스\n돈내코코스\n신흥리 동백마을\n감귤체험과 다양한 콘텐츠,  감귤박물관","producer":"Adobe PDF Library 18.0","trapped":"/False","total_pages":42,"page":24,"creator":"Adobe InDesign 21.0 (Windows)","source":"./data/제주관광가이드.pdf"}


created_at: 2026-06-01 04:13:05
topic: persistent://default/default/80d8d54a-809f-4b2e-89e9-7bb042a24d9e
id: 0cae4c88-cc72-43ba-b848-ebb57bfd3bae
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{"trapped":"/False","creationdate":"2026-03-12T11:22:44+09:00","source":"./data/제주관광가이드.pdf","producer":"Adobe PDF Library 18.0","page_label":"25","creator":"Adobe InDesign 21.0 (Windows)","page":24,"moddate":"2026-03-12T11:30:50+09:00","chroma:document":"관음사코스\n성판악코스\n돈내코코스\n신흥리 동백마을\n감귤체험과 다양한 콘텐츠,  감귤박물관\n제주 특산물인 감귤을 테마로 감귤의 세계를 한눈에 보고 느끼고 체험할 수 있는 전문 박물관이다. \n감귤의 역사와 문화를 엿볼 수 있도록 다양한 자료들이 전시되어 있다. 또한 인근 농장에서 \n진행하는 체험 프로그램들이 마련되어 있다. 영상으로 감귤에 대한 모든 궁금증을 해결할 수 \n있는 테마전시실을 비롯해, 민속유물전시실, 그리고 유리온실로 만들어진 세계감귤전시관과 \n아열대식물원에서 감귤 향 가득한 색다른 경험을 할 수 있다.\n겨울 제주 체험하기\n남쪽 섬 제주에도 겨울은 찾아온다. 시내권은 영하로 내려가는 날이 드물어 \n여행하기에 무리가 없다. 한라산에는 눈꽃이 내려앉고 잘 익은 귤은 수확의 손길을 \n기다린다. 계절을 잊은 듯 피어나는 동백은 겨울의 또 다른 낭만이다.\n겨울\n사계절 아름다운 제주   제주의 계절 관광지\n46JEJU   ISL AND   POCKET   BOOK —47","total_pages":42}


created_at: 2026-06-01 04:13:05
topic: persistent://default/default/80d8d54a-809f-4b2e-89e9-7bb042a24d9e
id: eccfaa12-5303-4986-887c-cee0054b8cfb
encoding: FLOAT32
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{"trapped":"/False","creationdate":"2026-03-12T11:22:44+09:00","producer":"Adobe PDF Library 18.0","source":"./data/제주관광가이드.pdf","total_pages":42,"page_label":"25","chroma:document":"46JEJU   ISL AND   POCKET   BOOK —47\nTravel  Jeju","page":24,"moddate":"2026-03-12T11:30:50+09:00","creator":"Adobe InDesign 21.0 (Windows)"}


created_at: 2026-06-01 04:13:05
topic: persistent://default/default/80d8d54a-809f-4b2e-89e9-7bb042a24d9e
id: 8168efbe-71c1-4665-a894-34e023332636
encoding: FLOAT32
metadata:
{"trapped":"/False","source":"./data/제주관광가이드.pdf","page":25,"creator":"Adobe InDesign 21.0 (Windows)","moddate":"2026-03-12T11:30:50+09:00","producer":"Adobe PDF Library 18.0","total_pages":42,"page_label":"26","creationdate":"2026-03-12T11:22:44+09:00","chroma:document":"에메랄드빛 바다와 오름. 제주의 풍경은 언제나 정겹고 평화롭다. 그러나 \n이토록 평화로운 제주가 일제강점기에는 일본 본토와 가깝다는 이유로 \n전략적인 전쟁 전초기지로 이용되었고, 또한 제주의 아픔인 4·3 항쟁의 \n불꽃에 휩싸이기도 했다. 제주의 항쟁의 역사와 아픔의 흔적을 따라가보자.\n알뜨르 비행장\n‘아래 있는 넓은 뜰’이라는 정겨운 이름을 가지고 있는 알뜨르. 농지 겸 목초지로 주민들의 삶의 \n터전이었던 이곳에 일본군들은 모슬포 주민들을 강제 동원해 군용 비행장을 건설하기 시작한다.  \n전쟁의 막바지, 일본이 감행한 ‘가미카제’ 훈련이 이뤄졌다고 전해진다.\n셋알오름 일제 진지동굴\n셋알오름에서 바라보는 경치는 그야말로 그림  \n같다. 그러나 이 아름다운 오름이 일제 강점기  \n전쟁을 준비하는 군사시설로 이용되었다는 사실은 \n아이러니하다. 1943년 일제가 연합군의 폭격으로부터 \n중요 군사 시설을 감추기 위해 구축한 동굴진지.\n너븐숭이 4.3기념관"}


created_at: 2026-06-01 04:13:05
topic: persistent://default/default/80d8d54a-809f-4b2e-89e9-7bb042a24d9e
id: 00b6288a-f40a-44f1-bf4f-7a7669849a0b
encoding: FLOAT32
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{"total_pages":42,"producer":"Adobe PDF Library 18.0","page":25,"page_label":"26","trapped":"/False","chroma:document":"중요 군사 시설을 감추기 위해 구축한 동굴진지.\n너븐숭이 4.3기념관\n1949년 1월17일, 북촌리 바로 옆 마을인 함덕에 주둔하던 군인 2명이 무장대의 습격을 받고 숨지는 \n일로 촉발됐다.\n 마\n을 전체가 불에 타고 400여 명이 희생됐다. 희생자 가운데 어린아이도 있었다. \n일가족이 몰살된 경우도 있었다. 이들을 추념하기 위해 너븐숭이에는 추모비와 작은 기념관이 들어서 \n있다. \n제주 4·3평화 공원\n제주 4·3 평화공원은 4·3 사건 당시의 희생자들을 기리기 위한 공간이다. 공원 안에는 제주 4·3 \n평화기념관, 위령제단, 위령탑, 봉안관 등이 이곳을 지키고 있다. 전시실을 통해 4·3의 발발, 전개, \n결과, 진상 규명 운동까지 전 과정이 차례로 펼쳐져 있어 자연스럽게 평화와 인권의 가치를 배울 수 \n있는 교육의 장으로 운영되고 있다.\n송악산 해안 일제 동굴진지\n송악산 아래로 깎아지를 듯한 해안절벽에 인위적으로 뚫어낸","creator":"Adobe InDesign 21.0 (Windows)","creationdate":"2026-03-12T11:22:44+09:00","source":"./data/제주관광가이드.pdf","moddate":"2026-03-12T11:30:50+09:00"}


created_at: 2026-06-01 04:13:05
topic: persistent://default/default/80d8d54a-809f-4b2e-89e9-7bb042a24d9e
id: 7a4aa0f4-3f9d-408b-af75-ae2ec70776b1
encoding: FLOAT32
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{"creationdate":"2026-03-12T11:22:44+09:00","page":25,"total_pages":42,"moddate":"2026-03-12T11:30:50+09:00","page_label":"26","producer":"Adobe PDF Library 18.0","creator":"Adobe InDesign 21.0 (Windows)","source":"./data/제주관광가이드.pdf","chroma:document":"송악산 해안 일제 동굴진지\n송악산 아래로 깎아지를 듯한 해안절벽에 인위적으로 뚫어낸 \n흔적은 보인다. 바로 일제 당시 구축된 해안 동굴진지이다. \n진지 구축을 위한 굴착 작업에는 많은 제주 주민들이 강제 \n동원 되어야 했다. 이유 모를 전쟁을 위해 목숨을 걸고 절벽을 \n뚫어야만 했던 주민들의 희생이 깃든 곳이다. 현재 15개의 진지가 \n남아있다.\n다크\n투어리즘\n곤을동 마을 터\n1949년 1월, 마을에 무장대원이 숨어들었다는 \n첩보를 입수한 진압군이\n 마\n을 전체를 불태우고 \n주민 수십 명이 학살됐다. 지금도 곤을동에는 당시 \n집터의 흔적이 뚜렷이 남아 있다. 곤을동 말고도 \n4·3 기간 중 진압군의 초토화 작전으로 중산간 \n마을 대부분이 불타 없어졌다.\n48\nJEJU   ISL AND   POCKET   BOOK— 49\nTravel  Jeju","trapped":"/False"}


created_at: 2026-06-01 04:13:05
topic: persistent://default/default/80d8d54a-809f-4b2e-89e9-7bb042a24d9e
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{"total_pages":42,"moddate":"2026-03-12T11:30:50+09:00","page":26,"source":"./data/제주관광가이드.pdf","creator":"Adobe InDesign 21.0 (Windows)","chroma:document":"난이도 ★★★☆ 경치 ★★★☆ \n3. 성판악 등산로  (9.6km, 4시간 30분 )\n숲의 정취를 듬뿍 느낄 수 있지만 긴 거리와 급경사로 \n쉽게 오르긴 어렵다. 맘먹고 한라산을 등산할 준비가 \n된 이들에게 추천한다. \n난이도 ★★★★☆ 경치 ★★★★\n4. 관음사 등산로  (8.7km, 5시간)\n급경사와 오르락 내리락하는 구간, 9km에 달하는 \n길이 등 한라산의 지옥코스로 유명하다. 다른  \n등산로보다 30% 이상 체력 소모를 하기 때문에 \n일반인보다는 전문 산악인들이 많이 찾는다. \n난이도 ★★☆ 경치 ★★★\n1. 영실 등산로  (5.8km, 2시간 30분)\n백록담으로 향하는 최단 코스로 비교적 오르기가 \n수월하다. 그러나 윗세오름까지만 개방돼 백록담까지 \n오를 수 없다. 관광을 목적으로 한 노부부와 신혼여행 \n중인 부부에게 제격이다. \n난이도 ★★★ 경치 ★★☆\n2. 어리목 등산로   (6.8km, 3시간)\n약간의 오르막길이 있지만 그리 힘들지 않다. 특히","trapped":"/False","producer":"Adobe PDF Library 18.0","page_label":"27","creationdate":"2026-03-12T11:22:44+09:00"}


created_at: 2026-06-01 04:13:05
topic: persistent://default/default/80d8d54a-809f-4b2e-89e9-7bb042a24d9e
id: b21c9f53-c33f-4d1e-958f-1a1440a0dfc1
encoding: FLOAT32
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{"total_pages":42,"page_label":"27","producer":"Adobe PDF Library 18.0","source":"./data/제주관광가이드.pdf","moddate":"2026-03-12T11:30:50+09:00","page":26,"creationdate":"2026-03-12T11:22:44+09:00","creator":"Adobe InDesign 21.0 (Windows)","trapped":"/False","chroma:document":"약간의 오르막길이 있지만 그리 힘들지 않다. 특히 \n겨울에 찾으면 넓게 펼쳐진 만세동산의 초지에 하얀 \n눈이 쌓여 연출되는 설경의 아름다움을 감상할 수 \n있다. 다만 윗세오름까지만 통행이 가능.\n난이도 ★★ 경치 ★★☆\n5. 돈내코 등산로 (7km, 3시간 30분)\n열대우림 지역의 정글을 닮은 우거진 산림과 완만한 \n코스가 자랑거리. 물이 마르지 않는 돈내코 계곡의 \n야영장에서 피서를 즐기는 것도 좋다. \n  한라산 트레킹\n수악계곡\n1139\n1117\n1139\n1131\n1131\n절물자연휴양림\n백록담\n1100도로\n1100도로\n1100고지\n휴게소\n산록도로\n도깨비도로\n사라오름\n남벽분기점\n평궤대피소\n산록도로\n산록도로\n서귀포자연휴양림\n병풍바위\n영실기암\n   윗세오름대피소\n어승생수련지\n탐라계곡\n대피소\n개미등\n진달래밭대피소\n속밭대피소\n사제비동산\n만세동산\n5.16도로\n삼각봉\n대피소\n돈내코유원지\n1115\n영실코스\n 어리목코스\n 관음사코스\n 성판악코스\n 돈내코코스\n1\n2\n4\n3\n5"}


created_at: 2026-06-01 04:13:05
topic: persistent://default/default/80d8d54a-809f-4b2e-89e9-7bb042a24d9e
id: bfb57ebf-b1fd-4a5d-b6b1-759d508682cd
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created_at: 2026-06-01 04:13:05
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{"trapped":"/False","moddate":"2026-03-12T11:30:50+09:00","source":"./data/제주관광가이드.pdf","total_pages":42,"page_label":"28","page":27,"creator":"Adobe InDesign 21.0 (Windows)","chroma:document":"더욱 자세한 올레길 정보는\n  올레길 탐방\n올레란 제주 방언으로 좁은 골목이란 뜻이며, 통상 큰길에서 집의 대문까지 이어지는 좁은 길을 \n말한다. 21코스로 구성된 트레일 코스 올레길을 걸으면 걸을수록 제주의 아름다움은 점점 커진다. \n한 걸음, 한 걸음. 제주의 속살을 만나자. \n 제주여행 버킷리스트\n애월\n한림\n한림항 비양도 도항선 선착장 \n저지마을회관용수포구\n무릉2리\n고산\n모슬봉\n신평 산방산 대평리\n송악산\n모슬포\n월평마을\n강정\n월드컵경기장\n비양도\n가파도\n차귀도\n마라도\n범섬\n제주국제공항 \n제주항 \n중문화순\n대정\n14코스\n추자도\n16코스\n18-1코스\n고산\n월드컵경기장월드컵경기장\n20km / 5~6시간 소요\n월평마을 출발\n12.9km / 3-4시간 소요\n4.2km / 1~2시간 소요\n15.6km / 5~6시간 소요\n17.3km / 5~6시간 소요\n16.2km / 4~5시간 소요\n19.9km / 6~7시간 소요\n9.3km / 3~4시간 소요\n11.4km / 4~5시간 소요","creationdate":"2026-03-12T11:22:44+09:00","producer":"Adobe PDF Library 18.0"}


created_at: 2026-06-01 04:13:05
topic: persistent://default/default/80d8d54a-809f-4b2e-89e9-7bb042a24d9e
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{"page":27,"source":"./data/제주관광가이드.pdf","total_pages":42,"creator":"Adobe InDesign 21.0 (Windows)","page_label":"28","trapped":"/False","producer":"Adobe PDF Library 18.0","creationdate":"2026-03-12T11:22:44+09:00","chroma:document":"9.3km / 3~4시간 소요\n11.4km / 4~5시간 소요\n15.8km / 5~6시간 소요\n17.5km / 5~6시간 소요\n15.7km / 4~5시간 소요11.8km / 3~4시간 소요\n서귀포 출발\n상동포구 출발\n화순금모래해수욕장 출발\n모슬포항 출발(하모체육공원)\n용수포구 출발\n저지리 예술정보화마을\n저지리 예술 정보화마을\n추자면사무소 출발\n고내포구 출발\n무릉생태학교 출발\n서귀포월드컵경기장 출발대평포구 출발\nA코스:16.5km / 5~6시간 소요\nB코스:13km / 4~5시간 소요\n한림항 비양도선착장 출발\n15코스\n18.9km / 6~7시간 소요\n광령1리사무소 출발\n17코스\n14-1 코스\n13코스\n12코스\n11코스\n9코스 7-1코스\n8코스 7코스10코스\n10-1코스\n쇠소깍\n위미 남원포구\n태흥\n토산망오름\n통오름\n혼인지\n온평포구\n광치기해변\n성산15.1km / 4~5시간소요\n11.3km / 3~4시간소요\n18.7km / 6~7시간소요\nA코스: 20.9km / 6~7시간 소요","moddate":"2026-03-12T11:30:50+09:00"}


created_at: 2026-06-01 04:13:05
topic: persistent://default/default/80d8d54a-809f-4b2e-89e9-7bb042a24d9e
id: 66a3dfdf-5927-4939-a049-b5b2f1944c81
encoding: FLOAT32
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{"creator":"Adobe InDesign 21.0 (Windows)","moddate":"2026-03-12T11:30:50+09:00","producer":"Adobe PDF Library 18.0","trapped":"/False","page_label":"28","creationdate":"2026-03-12T11:22:44+09:00","total_pages":42,"source":"./data/제주관광가이드.pdf","chroma:document":"18.7km / 6~7시간소요\nA코스: 20.9km / 6~7시간 소요\nB 코스: 14.6km / 4~5시간 소요\n13.2km / 4~5시간소요17.6km / 5~6시간소요19.4km / 6~7시간소요\n15.2km / 4~5시간소요\n시흥리정류장 출발\n제주해녀박물관 출발\n간세라운지\n(관덕정분식)출발\n온평포구 출발\n우도 천진항 출발김녕서포구 출발조천만세동산 출발\n광치기해변 출발\n시흥\n종달리\n가마리\n표선\n섭지코지\n섶섬\n조천\n함덕\n김녕\n우도세화\n18코스\n19코스 20코스\n21코스\n1-1코스\n1코스\n13.4km / 4~5시간 소요\n남원포구 출발\n19km / 5~6시간 소요\n표선해수욕장 출발\n10.1km / 3~4시간 소요\n쇠소깍 출발\n시흥시흥\n세화세화\n혼인지혼인지\n5코스 4코스6코스\n3코스\n2코스\n제주올레 06 4-762-2190\n18-2코스\n9.7km / 3~4시간 소요\n신양항 출발\n52JEJU   ISL AND   POCKET   BOOK —53\nTravel  Jeju","page":27}


created_at: 2026-06-01 04:13:05
topic: persistent://default/default/80d8d54a-809f-4b2e-89e9-7bb042a24d9e
id: 858b86bb-76ea-4f82-aa74-10e3a975b416
encoding: FLOAT32
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{"creator":"Adobe InDesign 21.0 (Windows)","total_pages":42,"page_label":"29","trapped":"/False","producer":"Adobe PDF Library 18.0","chroma:document":"출발 전에 챙겨야 할 것들\n주변 관광지와 맛집들이 소개되어 있는 제주 전도 / 무엇보다 \n안전이 중요! 자전거용 장갑, 무릎 압박 보호대, 헬멧 / 갑자기 \n비가 와도 걱정 없다. 우비, 모자, 가방 덮을 검은 비닐봉지 / \n배고픔을 달래줄 간단한 간식, 캔커피, 스포츠음료 / 야간 \n주행을 위한 전조등, 후미등 / 그 외 자전거 자물쇠, 짐칸용 \n밧줄, 비상 약품, 여행 기록장, 카메라 등\n  자전거 여행\n2~3시간 코스\n제주시 > 용두암 > 이호해안도로 > 도두봉 > 이호테우해수욕장(왕복)\n하루 코스\n제주시 > 용두암 > 이호해안도로 > 하귀~애월해안도로(천연돌염전, 다락쉼터) > 애월전망대 > \n협재해수욕장\n2박 3일 코스\n1박 : 제주시 > 용두암·용연 > 함덕해수욕장\n2박 : 김녕~성산해안도로(풍차마을, 철새도래지) > 우도(우도봉, 동안경굴, 서빈백사) > 섭지코지 > \n성산일출봉 > 다랑쉬오름 > 비자림 > 제주시\n3박4일 완주코스","source":"./data/제주관광가이드.pdf","creationdate":"2026-03-12T11:22:44+09:00","page":28,"moddate":"2026-03-12T11:30:50+09:00"}


created_at: 2026-06-01 04:13:05
topic: persistent://default/default/80d8d54a-809f-4b2e-89e9-7bb042a24d9e
id: 7c2c4037-fcdc-4cee-af6b-31dc06ba338b
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{"page_label":"29","trapped":"/False","page":28,"moddate":"2026-03-12T11:30:50+09:00","chroma:document":"성산일출봉 > 다랑쉬오름 > 비자림 > 제주시\n3박4일 완주코스\n1박 : 제주시 > 용두암~이호해안도로 > 하귀~애월해안도로(천연돌염전, 다락쉼터) > \n협재해수욕장(한림공원)\n2박 : 송악산 > 산방산·용머리해안 > 중문관광단지(주상절리, 쉬리의 언덕, 테디베어뮤지엄) > \n월드컵경기장\n3박 : 쇠소깍(소금악) > 남원큰엉해안경승지 > 제주민속촌박물관 > 김영갑갤러리 > 섭지코지 > \n성산일출봉 > 성산~김녕해안도로(풍차마을, 철새도래지) > 함덕해수욕장 > 용두암·용연 > 제주시\n그 밖에 제주에서는 다양한 코스에서 저렴하게 즐길 수 있는 골프, 다양한 어종과 함께 화끈한 \n손맛을 느낄 수 있는 바다낚시,  바다에서 최고의 스피드를 즐길 수 있는 제트보트, 여유로운 낭만을 \n느낄 수 있는 요트, 바다와 하늘을 오르내리는 파라세일링, 제주의 푸른 땅을 거침없이 달리는 ATV, \n남녀노소 누구나 안전운행이 가능한 카트, 11월 1일부터 다음 해 2월까지 4개월간 가능한 수렵","creator":"Adobe InDesign 21.0 (Windows)","creationdate":"2026-03-12T11:22:44+09:00","total_pages":42,"source":"./data/제주관광가이드.pdf","producer":"Adobe PDF Library 18.0"}


created_at: 2026-06-01 04:13:05
topic: persistent://default/default/80d8d54a-809f-4b2e-89e9-7bb042a24d9e
id: b0874b23-7fd3-4536-9f59-4434d1a3c9a0
encoding: FLOAT32
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{"producer":"Adobe PDF Library 18.0","chroma:document":"등을 즐길 수 있다.\n  레저 스포츠 여행\n스쿠버다이빙 푸른 물빛 아래로 풍덩 뛰어들다.\n연중 20도의 수온을 유지하는 바다와 아름다운  \n해저 풍경을 자랑하는 제주 문섬과 범섬 주변은  \n스쿠버다이빙을 즐기기에 제격이다.\n 수영실력과는 무관하므로 누구나 쉽게 도전할 수 있다\n유람선·잠수함 심연의 바다 여행\n세계적으로 희귀한 수중 생태계를 간직하고 있는 \n서귀포시 앞바다의 연산호 군락지를 볼 수 있는 제주바다 \n탐험은 절대 놓칠 수 없는 코스이다. 화산섬 제주의 \n빼어난 해저 지형을 눈에 담아 두고두고 기억하자.\n 뱃멀미 약을 준비하자.\n승마 너른 벌판을 달려라\n넓은 초원을 달리고 싶다면 제주에서의 승마를  \n추천한다. 말위에서 느끼는 제주의 바람은 또 다른 맛을 \n선사해 준다. \n 말에게 기습적인 뒷발길질을 당할 수 있으니 말 뒤에 서지 \n않도록 주의하자.\n패러글라이딩 푸른 하늘을 날아라 \n수많은 오름 위로 시원하게 불어주는 바람을 타고","moddate":"2026-03-12T11:30:50+09:00","page_label":"29","creator":"Adobe InDesign 21.0 (Windows)","source":"./data/제주관광가이드.pdf","trapped":"/False","total_pages":42,"creationdate":"2026-03-12T11:22:44+09:00","page":28}


created_at: 2026-06-01 04:13:05
topic: persistent://default/default/80d8d54a-809f-4b2e-89e9-7bb042a24d9e
id: e3cfae08-53e5-4226-8725-3c66f8690433
encoding: FLOAT32
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{"page_label":"29","creationdate":"2026-03-12T11:22:44+09:00","trapped":"/False","producer":"Adobe PDF Library 18.0","chroma:document":"패러글라이딩 푸른 하늘을 날아라 \n수많은 오름 위로 시원하게 불어주는 바람을 타고 \n새처럼 하늘을 날아보는 상상은 생각만으로도  \n짜릿함을 안겨준다. 제주에서라면 상상은 현실이 된다.\n 긴 소매 옷, 긴 바지는 필수!\n 제주여행 버킷리스트\n54JEJU   ISL AND   POCKET   BOOK —55\nTravel  Jeju","source":"./data/제주관광가이드.pdf","creator":"Adobe InDesign 21.0 (Windows)","moddate":"2026-03-12T11:30:50+09:00","total_pages":42,"page":28}


created_at: 2026-06-01 04:13:05
topic: persistent://default/default/80d8d54a-809f-4b2e-89e9-7bb042a24d9e
id: 44806d25-690a-4729-8676-f4d94d02c251
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{"creator":"Adobe InDesign 21.0 (Windows)","page":29,"chroma:document":"사려니숲길\n비자림로를 시작으로 물찻오름과 사려니 오름을 거쳐가는 삼나무가 우거진 숲길이다. 사려니 \n오름까지 이어지는 숲길이기 때문에 사려니숲길이라고 불린다. 가족들과 함께 천천히 걸어도 좋고 \n사색에 잠겨 혼자 걷는 것도 좋다.\n제주시 조천읍 교래리 ~ 서귀포시 남원읍 한남리 - 제주시험림(사려니오름방향)\n한라생태숲\n꿈꾸는 숲, 생명의 숲\n제주시 516로 2596(용강동)        \n064.710.8688\nwww.jeju.go.kr/hallaecoforest\n한라수목원\n숲이 주는 상쾌한 느낌\n제주시 수목원길 72(연동)\n0 6 4 -710 -7575\nsumokwon.jeju.go.kr\n서귀포자연휴양림\n나무와 숲이 함께하는 곳\n서귀포시 1100로 882(대포동) \n064.738.4544\nhealing.seogwipo.go.kr\n동백동산\n동백나무가 숲을 이루고 있는 천연림\n제주시 조천읍 동백로 77(동백동산습지센터)\n064.784.9445(안내소)\n  거문오름 탐방","producer":"Adobe PDF Library 18.0","source":"./data/제주관광가이드.pdf","trapped":"/False","total_pages":42,"creationdate":"2026-03-12T11:22:44+09:00","moddate":"2026-03-12T11:30:50+09:00","page_label":"30"}


created_at: 2026-06-01 04:13:05
topic: persistent://default/default/80d8d54a-809f-4b2e-89e9-7bb042a24d9e
id: fe1c0a9b-f8f7-4536-89d8-b4c4bc5b2caa
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{"creator":"Adobe InDesign 21.0 (Windows)","page":29,"source":"./data/제주관광가이드.pdf","moddate":"2026-03-12T11:30:50+09:00","chroma:document":"064.784.9445(안내소)\n  거문오름 탐방\n세계적으로 보전가치를 인정받아 유네스코 세계자연유산에 등재된 거문오름. 제주의 생태, \n지리학적 특징들이 그대로 남아있다. 탐방은 예약제로 운영된다. 탐방을 원하는 여행객은 예약필수.\n제주시 조천읍 선흘리\n064.710.8981 (거문오름탐방안내소)\nhttps://www.jeju.go.kr/wnhcenter\n한라산둘레길\n자연을 느끼며 걷는 길\n코스별로 상이\n064.784.4280  \nhallatrail.or.kr\n제주절물자연휴양림\n휴양과 치유의 숲\n제주시 명림로584\n06 4.728.1510\njeolmul.jejusi.go.kr\n서귀포치유의숲\n마음의 안정을 주는 힐링숲 \n서귀포시 산록남로 2271\n064.760.3067~8\nhealing.seogwipo.go.kr\n비자림\n사계절 내내 푸르른 비자나무 숲\n제주시 구좌읍 비자숲길 55          \n064.710.7912\n탐방로 정보와 예약\n  제주의 숲길\n 제주여행 버킷리스트","creationdate":"2026-03-12T11:22:44+09:00","producer":"Adobe PDF Library 18.0","trapped":"/False","total_pages":42,"page_label":"30"}


created_at: 2026-06-01 04:13:05
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{"creator":"Adobe InDesign 21.0 (Windows)","page_label":"30","source":"./data/제주관광가이드.pdf","trapped":"/False","producer":"Adobe PDF Library 18.0","page":29,"moddate":"2026-03-12T11:30:50+09:00","creationdate":"2026-03-12T11:22:44+09:00","total_pages":42,"chroma:document":"064.710.7912\n탐방로 정보와 예약\n  제주의 숲길\n 제주여행 버킷리스트\n56JEJU   ISL AND   POCKET   BOOK —57\nTravel  Jeju"}


created_at: 2026-06-01 04:13:05
topic: persistent://default/default/80d8d54a-809f-4b2e-89e9-7bb042a24d9e
id: b2cfd85d-b14c-43c1-a35a-63a94dbbd4f0
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{"page":30,"chroma:document":"감귤가공식품\n그냥 먹어도 맛 좋은 제주의 특산품 감귤. 이젠 더욱 특별하게 \n즐겨보자.  감귤을 원료 한 달콤한 \n잼이나 콩포트, 간식으로 좋은 \n감귤파이와 초콜릿 등. 감귤을 활용한 \n다양한 가공식품이 있다.\n돌하르방\n돌하르방은 현무암으로 깎아서 만든 제주도 \n특유의 석상이며 제주도의 대표적인  \n랜드마크이다. 각종 기념품으로 가공되어 \n판매되고 있다.\n우도 땅콩\n제주의 섬 속의 섬 우도. 우도에서 생산되는 우도\n땅콩은 일반 땅콩과는 다르다. 모양이 좀 더 \n둥글고 훨씬 고소한 맛을 뽐낸다. 초콜릿 등 우도\n땅콩을 활용한 각종 식품들 역시 인기.\n과즐\n제주의 전통 한과 과즐. 감귤을 사용해 \n은은한 단맛과 고소한 바삭함이 일품이다. \n소박해 보이지만 한번 맛보면 계속 먹게 \n되는 마성의 과자.\n제주 디자인 문구\n제주를 소재로 한 다양한 문구류가 인기다. \n노트와 펜, 달력과 엽서 등 각종 문구류와 제주의 \n감성이 더해져 저마다 개성을 뽐내고 있다.\n향초, 방향제, 수제비누","page_label":"31","moddate":"2026-03-12T11:30:50+09:00","creator":"Adobe InDesign 21.0 (Windows)","total_pages":42,"source":"./data/제주관광가이드.pdf","trapped":"/False","producer":"Adobe PDF Library 18.0","creationdate":"2026-03-12T11:22:44+09:00"}


created_at: 2026-06-01 04:13:05
topic: persistent://default/default/80d8d54a-809f-4b2e-89e9-7bb042a24d9e
id: 597e0111-722d-407e-b728-651aba1caf8d
encoding: FLOAT32
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{"source":"./data/제주관광가이드.pdf","total_pages":42,"page_label":"31","moddate":"2026-03-12T11:30:50+09:00","producer":"Adobe PDF Library 18.0","creator":"Adobe InDesign 21.0 (Windows)","trapped":"/False","page":30,"creationdate":"2026-03-12T11:22:44+09:00","chroma:document":"감성이 더해져 저마다 개성을 뽐내고 있다.\n향초, 방향제, 수제비누\n작은 공방에서 직접 제작하는 수제 캔들과 방향제, 비누는 \n감성적이고 실용적이다. 제주의 바다와 해녀, 한라산 등 \n제주에서 얻은 모티브로 제작해 감성을 더한다.\n제주감귤\n제주의 대표 특산물 감귤. 달콤하고 상큼한 맛이 \n일품이다. 종자를 개량한 한라봉이나, 천혜향, \n황금향 등은 더욱 고급스러운 향과 맛을 자랑한다. \n제주에 여행 왔다면 꼭 먹어보고 선물로도 손색이 \n없다\n흑돼지 육포, 말고기 육포\n감귤과 더불어 흑돼지와 말이 유명한 제주도. 자극적이지 않고 고소한 \n맛으로 인기몰이 중인 육포가 있다. 간식이나 맥주 안주로 즐겨보자.\n제주여행\n쇼핑리스트\n58JEJU   ISL AND   POCKET   BOOK —59\nTravel  Jeju"}


created_at: 2026-06-01 04:13:05
topic: persistent://default/default/80d8d54a-809f-4b2e-89e9-7bb042a24d9e
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{"creationdate":"2026-03-12T11:22:44+09:00","trapped":"/False","producer":"Adobe PDF Library 18.0","chroma:document":"2박 3일  : 제주 서부\n추천\n여행코스\n02\n제주국제공항\n안덕계곡\n한담해안산책로\n대포주상\n절리대\n새별오름\n약천사\n제주곶자왈\n도립공원\n외돌개\n오설록\n천지연폭포\n(새연교)야경관광\n숙박 : 중문\n숙박 : \n서귀포시내\n1일\n2일\n사려니숲길 절물자연휴양림 제주돌문화공원 제주동문시장 제주국제공항\n3일\n2박 3일  : 제주 동부\n추천\n여행코스\n01\n제주국제공항\n성산일출봉\n월정리해변\n빛의 벙커\n(커피박물관 바움)\n별방진\n성읍민속마을\n제주해녀박물관\n or 비자림\n쇠소깍\n아부오름\nor 용눈이오름\n천지연폭포\n(새연교)야경관광\n숙박 : 성산\n숙박 : \n서귀포시내\n1일\n2일\n이중섭거리 서귀포매일\n올레시장\n사려니숲길 도두봉/\n무지개해안도로\n제주국제공항\n3일\n60JEJU   ISL AND   POCKET   BOOK —61\nTravel  Jeju","total_pages":42,"moddate":"2026-03-12T11:30:50+09:00","creator":"Adobe InDesign 21.0 (Windows)","page_label":"32","source":"./data/제주관광가이드.pdf","page":31}


created_at: 2026-06-01 04:13:05
topic: persistent://default/default/80d8d54a-809f-4b2e-89e9-7bb042a24d9e
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{"moddate":"2026-03-12T11:30:50+09:00","trapped":"/False","total_pages":42,"page_label":"33","chroma:document":"3일\n성산일출봉 사려니숲길 절물자연\n휴양림\n제주\n돌문화공원\n도두봉/무지개\n해안도로\n제주국제공항\n4일\n외돌개 서귀포매일\n올레시장\n이중섭거리 천지연폭포\n(새연교)야경관광\n숙박 : \n서귀포 시내\n3박 4일  : 서부→동부\n추천\n여행코스\n03\n제주국제공항 한담해안산책로 협재/금능\n해수욕장\n한림공원 수월봉&\n엉알해안산책로\n숙박 : 서부\n1일\n오설록 산방산\n용머리해안\n천제연폭포 약천사 대포주상\n절리대\n숙박 : 중문\n2일\n62JEJU   ISL AND   POCKET   BOOK —63\nTravel  Jeju","creationdate":"2026-03-12T11:22:44+09:00","creator":"Adobe InDesign 21.0 (Windows)","page":32,"source":"./data/제주관광가이드.pdf","producer":"Adobe PDF Library 18.0"}


created_at: 2026-06-01 04:13:05
topic: persistent://default/default/80d8d54a-809f-4b2e-89e9-7bb042a24d9e
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{"source":"./data/제주관광가이드.pdf","trapped":"/False","creationdate":"2026-03-12T11:22:44+09:00","total_pages":42,"moddate":"2026-03-12T11:30:50+09:00","page_label":"34","producer":"Adobe PDF Library 18.0","page":33,"creator":"Adobe InDesign 21.0 (Windows)","chroma:document":"3일\n산방산/\n용머리해안\n오설록 새별오름 용담해안도로 제주국제공항\n4일\n외돌개 서귀포매일\n올레시장\n이중섭거리 약천사 대포주상\n절리대\n숙박 : 중문\n3박 4일  : 동부→서부\n추천\n여행코스\n04\n제주국제공항 월정리해변 별방진 제주해녀박물관\n or 비자림\n아부오름\nor 용눈이오름\n숙박 : 성산\n1일\n2일\n성산일출봉 빛의 벙커\n(커피박물관 바움)\n성읍민속마을 쇠소깍 천지연폭포\n(새연교)야경관광\n숙박 : \n서귀포시내\n64JEJU   ISL AND   POCKET   BOOK —65\nTravel  Jeju"}


created_at: 2026-06-01 04:13:05
topic: persistent://default/default/80d8d54a-809f-4b2e-89e9-7bb042a24d9e
id: 35f6fa73-5022-49b3-ba89-3e92c0abb689
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{"page_label":"35","producer":"Adobe PDF Library 18.0","page":34,"creator":"Adobe InDesign 21.0 (Windows)","total_pages":42,"creationdate":"2026-03-12T11:22:44+09:00","chroma:document":"쇠소깍 남원큰엉해안\n경승지\n성읍민속마을 김영갑갤러리\n두모악\n섭지코지 숙박:성산\n4일\n성산일출봉 사려니숲길\nor절물자연휴양림\n제주돌문화공원 도두봉/무지개\n해안도로\n제주국제공항\n5일\n3일\n외돌개 서귀포매일\n올레시장\n이중섭거리 천지연폭포\n(새연교)야경관광\n숙박 : \n서귀포 시내\n본 일정은 제주관광정보센터에서 안내하는 표준 일정으로 064-740-6000으로 \n문의하시면 여행자별 맞춤 일정을 안내해 드립니다. 한·중·일·영으로 구성된 전문 \n상담직원이 바로 응대해 제주 여행에 관한 모든 안내를 제공합니다. \n상담시간 : 09:00 ~ 18:00 (연중무휴, 한국 시간 기준)\n4박 5일\n추천\n여행코스\n05\n제주국제공항 한담해안산책로 협재/금능\n해수욕장\n한림공원 수월봉&\n엉알해안산책로\n숙박 : 서부\n1일\n제주곶자왈\n도립공원\n오설록 천제연폭포 약천사 대포주상\n절리대\n숙박 : 중문\n2일\n66JEJU   ISL AND   POCKET   BOOK —67\nTravel  Jeju","source":"./data/제주관광가이드.pdf","trapped":"/False","moddate":"2026-03-12T11:30:50+09:00"}


created_at: 2026-06-01 04:13:05
topic: persistent://default/default/80d8d54a-809f-4b2e-89e9-7bb042a24d9e
id: 922d3d56-9b82-4afe-9c28-7f52efd7645c
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{"chroma:document":"6월 June\n곶자왈 반딧불이 축제\n청수리 곶자왈 064.772.1303\n산양 큰엉곶 반딧불이 탐방\n산양 큰엉곶\n064.772.4229\n용연음악회\n제주시 용연 일원\n064.728.2714\n7월 July\n이호테우축제\n이호테우해변 일원 \n064.728.4931\n삼양검은모래축제\n삼양검은모래해변 064.728.4713\n8월  August\n제주국제관악제\n제주시, 서귀포시\n064.722.8704\n예래생태마을체험축제\n논짓물, 예래생태체험관 일원\n064.760.4864\n도두오래물축제\n도두항 일원\n064.712.4597\n9월  September\n산지천축제\n산지천 일원\n064.728.4656\n서귀포칠십리축제\n서귀포 자구리공원 일대\n064.760.2656\n봉개오름축제\n봉개여가문화공원\n064.728.4742\n제주해녀축제\n해녀박물관\n064.782.9898\n10월  October\n제주마축제\n렛츠런파크 제주\n064.786.0114\n덕수리 전통민속재현축제\n덕수리민속공연장\n064.760.2482\n탐라문화제","source":"./data/제주관광가이드.pdf","creator":"Adobe InDesign 21.0 (Windows)","page_label":"36","page":35,"total_pages":42,"moddate":"2026-03-12T11:30:50+09:00","producer":"Adobe PDF Library 18.0","creationdate":"2026-03-12T11:22:44+09:00","trapped":"/False"}


created_at: 2026-06-01 04:13:05
topic: persistent://default/default/80d8d54a-809f-4b2e-89e9-7bb042a24d9e
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{"source":"./data/제주관광가이드.pdf","total_pages":42,"page_label":"36","producer":"Adobe PDF Library 18.0","creationdate":"2026-03-12T11:22:44+09:00","creator":"Adobe InDesign 21.0 (Windows)","page":35,"moddate":"2026-03-12T11:30:50+09:00","chroma:document":"덕수리 전통민속재현축제\n덕수리민속공연장\n064.760.2482\n탐라문화제\n제주도 일원\n064.753.3287\n혼인지 축제\n온평리 혼인지 일대원\n064.782.2766\n11월  November\n최남단 방어축제\n대정읍 모슬포항 일원\n064.760.4082\n제주감귤박람회\n서귀포농업기술센터 등\n064.710.3091~5\n제주옹기굴제\n고바치노랑굴 일대\n1899.4008\n12월  December\n한림공원 국화축제\n한림공원\n064.796.0001~4\n성산일출축제\n성산일출봉일원\n064.760.4281~4\n상기 일정과 문의처는 상황에 따라 변동될 수 있습니다.\n제주의 축제\n1월 January\n한림공원 수선화축제\n한림공원\n064.796.0001~4\n서귀포 겨울바다펭귄수영대회\n중문색달해변\n064.760.3320\n2월  February\n탐라국 입춘굿놀이\n제주목관아, 구도심\n064.758.0332\n노리매 매화축제\n제주도 노리매공원 내\n064.792.8211~4\n제주민속촌 설날맞이 민속행사\n제주민속촌","trapped":"/False"}


created_at: 2026-06-01 04:13:05
topic: persistent://default/default/80d8d54a-809f-4b2e-89e9-7bb042a24d9e
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{"source":"./data/제주관광가이드.pdf","total_pages":42,"creationdate":"2026-03-12T11:22:44+09:00","trapped":"/False","moddate":"2026-03-12T11:30:50+09:00","producer":"Adobe PDF Library 18.0","page":35,"page_label":"36","creator":"Adobe InDesign 21.0 (Windows)","chroma:document":"제주도 노리매공원 내\n064.792.8211~4\n제주민속촌 설날맞이 민속행사\n제주민속촌\n064.787.4501\n한림공원 매화축제\n한림공원\n064.796.0001~4\n휴애리 매화축제\n휴애리자연생활공원\n064.732.2114    \n3월  March\n유채꽃 국제걷기대회\n제주월드컵경기장\n064.760.3320\n한림공원 튤립축제\n한림공원\n064.796.0001~4\n4월  April\n한라산 청정고사리축제\n남원읍 한남리\n064.760.4182\n제주유채꽃축제\n조랑말체험공원\n064.760.4462\n제주왕벚꽃축제\n제주시 일원\n064.728.2753\n가파도청보리축제\n서귀포시 가파리 일원\n064.760.4082\n5월  May\n방선문축제\n방선문계곡 일원\n064.728.4802\n보목자리돔 축제\n보목항 일원\n064.760.4503\n국제e-모빌리티엑스포\n제주국제컨벤션센터\n064.702.1580\n68JEJU   ISL AND   POCKET   BOOK —69\nInformation"}


created_at: 2026-06-01 04:13:05
topic: persistent://default/default/80d8d54a-809f-4b2e-89e9-7bb042a24d9e
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created_at: 2026-06-01 04:13:05
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created_at: 2026-06-01 04:13:05
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created_at: 2026-06-01 04:13:05
topic: persistent://default/default/80d8d54a-809f-4b2e-89e9-7bb042a24d9e
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created_at: 2026-06-01 04:13:05
topic: persistent://default/default/80d8d54a-809f-4b2e-89e9-7bb042a24d9e
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created_at: 2026-06-01 04:13:05
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created_at: 2026-06-01 04:13:05
topic: persistent://default/default/80d8d54a-809f-4b2e-89e9-7bb042a24d9e
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created_at: 2026-06-01 04:13:05
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created_at: 2026-06-01 06:01:44
topic: persistent://default/default/d94e1968-cc22-40f0-8810-2412728b970e
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{"author":"","chroma:document":"Summary of ChatGPT/GPT-4 Research\nand Perspective Towards the Future of Large\nLanguage Models\nYiheng Liu ∗1, Tianle Han ∗1, Siyuan Ma 1, Jiayue Zhang 1,\nYuanyuan Yang1, Jiaming Tian 1, Hao He 1, Antong Li 2, Mengshen\nHe1, Zhengliang Liu 3, Zihao Wu 3, Dajiang Zhu 4, Xiang Li 5, Ning\nQiang1, Dingang Shen 6,7,8, Tianming Liu 3, and Bao Ge †1\n1School of Physics and Information Technology, Shaanxi Normal University, Xi’an\n710119 China\n2School of Life and Technology Biomedical-Engineering, Xi’an Jiaotong University,\nXi’an 710049, China\n3School of Computing, The University of Georgia, Athens 30602, USA\n4Department of Computer Science and Engineering, The University of Texas at\nArlington, Arlington 76019, USA\n5Department of Radiology, Massachusetts General Hospital and Harvard Medical\nSchool, Boston 02115, USA\n6School of Biomedical Engineering, ShanghaiTech University, Shanghai 201210,\nChina\n7Shanghai United Imaging Intelligence Co., Ltd., Shanghai 200230, China","producer":"pdfTeX-1.40.21","page_label":"1","moddate":"2023-04-05T00:33:07+00:00","title":"","keywords":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","creationdate":"2023-04-05T00:33:07+00:00","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","page":0,"subject":"","creator":"LaTeX with hyperref","total_pages":35,"trapped":"/False"}


created_at: 2026-06-01 06:01:44
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{"trapped":"/False","chroma:document":"China\n7Shanghai United Imaging Intelligence Co., Ltd., Shanghai 200230, China\n8Shanghai Clinical Research and Trial Center, Shanghai 201210, China\nAbstract\nThis paper presents a comprehensive survey of ChatGPT and GPT-4,\nstate-of-the-art large language models (LLM) from the GPT series, and\ntheir prospective applications across diverse domains. Indeed, key innova-\ntions such as large-scale pre-training that captures knowledge across the\nentire world wide web, instruction ﬁne-tuning and Reinforcement Learn-\ning from Human Feedback (RLHF) have played signiﬁcant roles in en-\nhancing LLMs’ adaptability and performance. We performed an in-depth\nanalysis of 194 relevant papers on arXiv, encompassing trend analysis,\nword cloud representation, and distribution analysis across various appli-\ncation domains. The ﬁndings reveal a signiﬁcant and increasing interest\nin ChatGPT/GPT-4 research, predominantly centered on direct natural","author":"","moddate":"2023-04-05T00:33:07+00:00","creator":"LaTeX with hyperref","title":"","total_pages":35,"subject":"","page_label":"1","producer":"pdfTeX-1.40.21","keywords":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","creationdate":"2023-04-05T00:33:07+00:00","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","page":0}


created_at: 2026-06-01 06:01:44
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{"chroma:document":"in ChatGPT/GPT-4 research, predominantly centered on direct natural\nlanguage processing applications, while also demonstrating considerable\npotential in areas ranging from education and history to mathematics,\n∗Co-ﬁrst author\n†Corresponding author: bob ge@snnu.edu.cn\n1\narXiv:2304.01852v1  [cs.CL]  4 Apr 2023","total_pages":35,"source":"./data/Summary of ChatGPTGPT-4 Research.pdf","page":0,"keywords":"","page_label":"1","moddate":"2023-04-05T00:33:07+00:00","creator":"LaTeX with hyperref","subject":"","creationdate":"2023-04-05T00:33:07+00:00","author":"","trapped":"/False","title":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","producer":"pdfTeX-1.40.21"}


created_at: 2026-06-01 06:01:44
topic: persistent://default/default/d94e1968-cc22-40f0-8810-2412728b970e
id: 24bb41cf-e92e-428b-873d-b2ce1afc46ff
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{"creator":"LaTeX with hyperref","title":"","moddate":"2023-04-05T00:33:07+00:00","chroma:document":"medicine, and physics. This study endeavors to furnish insights into Chat-\nGPT’s capabilities, potential implications, ethical concerns, and oﬀer di-\nrection for future advancements in this ﬁeld.\n1 Introduction\nRecent advances in natural language processing (NLP) have led to the devel-\nopment of powerful language models such as the GPT (Generative Pre-trained\nTransformer) series [1, 2, 3, 4, 5], including large language models (LLM) such\nas ChatGPT and GPT-4. These models are pre-trained on vast amounts of\ntext data and have demonstrated exceptional performance in a wide range of\nNLP tasks, including language translation, text summarization, and question-\nanswering. In particular, the ChatGPT model has demonstrated its potential\nin various ﬁelds, including education, healthcare, reasoning, text generation,\nhuman-machine interaction, and scientiﬁc research.\nA key milestone of LLM development is InstructGPT [5], a framework that","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","author":"","total_pages":35,"page_label":"2","page":1,"producer":"pdfTeX-1.40.21","trapped":"/False","subject":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","creationdate":"2023-04-05T00:33:07+00:00","keywords":""}


created_at: 2026-06-01 06:01:44
topic: persistent://default/default/d94e1968-cc22-40f0-8810-2412728b970e
id: 4e454df6-bbd8-4650-9ea4-526a8e19ac39
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{"creationdate":"2023-04-05T00:33:07+00:00","total_pages":35,"keywords":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","page":1,"author":"","producer":"pdfTeX-1.40.21","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","page_label":"2","trapped":"/False","chroma:document":"A key milestone of LLM development is InstructGPT [5], a framework that\nallows for instruction ﬁne-tuning of a pre-trained language model based on Re-\ninforcement Learning from Human Feedback (RLHF) [6, 5]. This framework\nenables an LLM to adapt to a wide range of NLP tasks, making it highly ver-\nsatile and ﬂexible by leveraging human feedback. RLHF enables the model to\nalign with human preferences and human values, which signiﬁcantly improves\nfrom large language models that are solely trained text corpora through unsu-\npervised pre-training. ChatGPT is a successor to InstructGPT. Since its release\nin December 2022, ChatGPT has been equipped with these advanced develop-\nments, leading to impressive performances in various downstream NLP tasks\nsuch as reasoning and generalized text generation. These unprecedented NLP\ncapabilities spur applications in diverse domains such as education, healthcare,\nhuman-machine interaction, medicine and scientiﬁc research. ChatGPT has re-","title":"","creator":"LaTeX with hyperref","subject":"","moddate":"2023-04-05T00:33:07+00:00"}


created_at: 2026-06-01 06:01:44
topic: persistent://default/default/d94e1968-cc22-40f0-8810-2412728b970e
id: 25aaf3a2-8417-4dcf-94d5-c6dbcd0de15f
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{"producer":"pdfTeX-1.40.21","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","page":1,"page_label":"2","total_pages":35,"chroma:document":"human-machine interaction, medicine and scientiﬁc research. ChatGPT has re-\nceived widespread attention and interest, leading to an increasing number of\napplications and research that harness its exceeding potential. The open release\nof the multi-modal GPT-4 model further expands the horizon of large language\nmodels and empowers exciting developments that involve diverse data beyond\ntext.\nThe purpose of this paper is to provide a comprehensive survey of the existing\nresearch on ChatGPT and its potential applications in various ﬁelds. To achieve\nthis goal, we conducted a thorough analysis of papers related to ChatGPT in\nthe arXiv repository. As of April 1st, 2023, there are a total of 194 papers\nmentioning ChatGPT on arXiv. In this study, we conducted a trend analysis of\nthese papers and generated a word cloud to visualize the commonly used terms.\nAdditionally, we also examined the distribution of the papers across various","creationdate":"2023-04-05T00:33:07+00:00","title":"","keywords":"","trapped":"/False","author":"","creator":"LaTeX with hyperref","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","moddate":"2023-04-05T00:33:07+00:00","subject":""}


created_at: 2026-06-01 06:01:44
topic: persistent://default/default/d94e1968-cc22-40f0-8810-2412728b970e
id: e3bd4961-93ec-45f5-b49d-e3b4bb106103
encoding: FLOAT32
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{"creationdate":"2023-04-05T00:33:07+00:00","page_label":"2","trapped":"/False","moddate":"2023-04-05T00:33:07+00:00","page":1,"total_pages":35,"subject":"","title":"","chroma:document":"Additionally, we also examined the distribution of the papers across various\nﬁelds and presented the corresponding statistics. Figure 1 displays the daily\nsubmission trend of papers related to ChatGPT, indicating a growing interest\nin this ﬁeld. Figure 2 illustrates the word cloud analysis of all the papers. We\ncan observe that the current research is primarily focused on natural language\nprocessing, but there is still signiﬁcant potential for research in other ﬁelds such\n2","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","producer":"pdfTeX-1.40.21","author":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","keywords":"","creator":"LaTeX with hyperref"}


created_at: 2026-06-01 06:01:44
topic: persistent://default/default/d94e1968-cc22-40f0-8810-2412728b970e
id: ac83a40b-b2c7-400a-9c36-33c55b2e1b31
encoding: FLOAT32
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{"creator":"LaTeX with hyperref","producer":"pdfTeX-1.40.21","moddate":"2023-04-05T00:33:07+00:00","chroma:document":"Figure 1: The number of papers submitted by researchers per day.\n3","title":"","creationdate":"2023-04-05T00:33:07+00:00","keywords":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","page_label":"3","trapped":"/False","author":"","total_pages":35,"subject":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","page":2}


created_at: 2026-06-01 06:01:44
topic: persistent://default/default/d94e1968-cc22-40f0-8810-2412728b970e
id: 5acad387-c5ef-4482-b3e1-45e263097f8c
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{"title":"","chroma:document":"Figure 2: Word cloud analysis of all the 194 papers.\nas education and history. This is further supported by Figure 3, which displays\nthe distribution of submitted papers across various ﬁelds, highlighting the need\nfor more research and development in these areas.\nThis paper aims to shed light on the promising capabilities of ChatGPT\nand provide insight into its potential impact in the future, including ethical\nconsiderations. Through this survey, we hope to provide insights into how these\nmodels can be improved and extended in the future. In section 2, we will\nreview the existing work related to ChatGPT, including its applications, ethical\nconsiderations, and evaluation. In addition to discussing the current state of\nresearch related to ChatGPT, we will also explore its limitations in section 3.\nFurthermore, we will provide guidance on future directions for language model\ndevelopment.\n2 Related work of ChatGPT","author":"","trapped":"/False","producer":"pdfTeX-1.40.21","total_pages":35,"ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","keywords":"","page":3,"moddate":"2023-04-05T00:33:07+00:00","page_label":"4","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","subject":"","creator":"LaTeX with hyperref","creationdate":"2023-04-05T00:33:07+00:00"}


created_at: 2026-06-01 06:01:44
topic: persistent://default/default/d94e1968-cc22-40f0-8810-2412728b970e
id: 28915d4a-865a-4246-bb72-a78ccf1e4803
encoding: FLOAT32
metadata:
{"moddate":"2023-04-05T00:33:07+00:00","trapped":"/False","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","keywords":"","producer":"pdfTeX-1.40.21","creator":"LaTeX with hyperref","subject":"","author":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","page":3,"total_pages":35,"chroma:document":"development.\n2 Related work of ChatGPT\nIn this section, we review the latest research related to the application, ethics,\nand evaluation of ChatGPT.\n2.1 Application of ChatGPT\n2.1.1 Question And Answering\nIn the education ﬁeld\nChatGPT is commonly used for question and answers testing in the edu-\ncation sector. Users can use ChatGPT to learn, compare and verify answers\nfor diﬀerent academic subjects such as physics, mathematics, and chemistry,\n4","page_label":"4","title":"","creationdate":"2023-04-05T00:33:07+00:00"}


created_at: 2026-06-01 06:01:44
topic: persistent://default/default/d94e1968-cc22-40f0-8810-2412728b970e
id: 37c3331a-1aaa-4d1a-af38-6c5be3cfae9b
encoding: FLOAT32
metadata:
{"page":4,"producer":"pdfTeX-1.40.21","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","keywords":"","subject":"","chroma:document":"Figure 3: The distribution of submitted papers across various ﬁelds.\n5","creationdate":"2023-04-05T00:33:07+00:00","total_pages":35,"moddate":"2023-04-05T00:33:07+00:00","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","page_label":"5","creator":"LaTeX with hyperref","trapped":"/False","author":"","title":""}


created_at: 2026-06-01 06:01:44
topic: persistent://default/default/d94e1968-cc22-40f0-8810-2412728b970e
id: 26390ad1-8dfd-4ee4-8655-f90f04195719
encoding: FLOAT32
metadata:
{"author":"","title":"","keywords":"","page_label":"6","moddate":"2023-04-05T00:33:07+00:00","creationdate":"2023-04-05T00:33:07+00:00","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","page":5,"subject":"","creator":"LaTeX with hyperref","total_pages":35,"producer":"pdfTeX-1.40.21","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","trapped":"/False","chroma:document":"and/or conceptual subjects such as philosophy and religion. Additionally, users\ncan ask open-ended and analytical questions to understand the capabilities of\nChatGPT.\nIn the ﬁeld of mathematics, Frieder et al. [7] constructed the GHOSTS nat-\nural language dataset, which consists of graduate-level math test questions.\nThe authors tested ChatGPT’s math abilities on the GHOSTS dataset us-\ning a question-and-answer format and evaluated it according to ﬁne-grained\nstandards.In the Grad Text dataset, which covers simple set theory and logic\nproblems, ChatGPT performed the best. However, in the Olympiad-Problem-\nSolving dataset, ChatGPT performed poorly, receiving only two 4-point scores\n(out of a total of 5), with the majority of scores being 2 points. In the Holes-\nin-Proofs dataset, ChatGPT received the lowest score of 1 point. In the MATH\ndataset, ChatGPT only scored impressively in 26% of cases. These results sug-"}


created_at: 2026-06-01 06:01:44
topic: persistent://default/default/d94e1968-cc22-40f0-8810-2412728b970e
id: e0057f32-af43-4208-b484-3cf3dd190a6e
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{"source":"./data/Summary of ChatGPTGPT-4 Research.pdf","moddate":"2023-04-05T00:33:07+00:00","chroma:document":"dataset, ChatGPT only scored impressively in 26% of cases. These results sug-\ngest that ChatGPT’s math abilities are clearly lower than those of ordinary\nmath graduate students. Although ChatGPT can generally understand math\nproblems, it fails to provide the correct solutions. Pardos et al. [8] used the\nOpen Adaptive Tutoring system (OATutor) to investigate whether prompts gen-\nerated by ChatGPT were helpful for learning algebra, with 77 participants from\nMechanical Turk taking part in the experiment. The experiment used ques-\ntions from OpenStax’s Elementary and Intermediate Algebra textbooks. These\nparticipants were randomly assigned to either a control group (with manual\nprompts) or an experimental group (with ChatGPT prompts). For each ques-\ntion in both courses, the authors obtained answers from ChatGPT through a\nquestion-and-answer format and evaluated scores according to three criteria:\nChatGPT provided an answer, the answer was correct, and inappropriate lan-","author":"","total_pages":35,"creationdate":"2023-04-05T00:33:07+00:00","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","page":5,"subject":"","creator":"LaTeX with hyperref","trapped":"/False","title":"","keywords":"","page_label":"6","producer":"pdfTeX-1.40.21"}


created_at: 2026-06-01 06:01:44
topic: persistent://default/default/d94e1968-cc22-40f0-8810-2412728b970e
id: 1529a510-1b4f-4853-9358-82d72f932822
encoding: FLOAT32
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{"chroma:document":"ChatGPT provided an answer, the answer was correct, and inappropriate lan-\nguage was not used in the answer. The study found that 70% of prompts gener-\nated by ChatGPT passed manual quality checks, and both humans and Chat-\nGPT produced positive learning gains. However, the scores of human prompts\nranged from 74.59% to 84.32%, signiﬁcantly higher than those of ChatGPT\nprompts. Shakarian et al. [9] studied the performance of ChatGPT on math\nword problems (MWPs), using the DRAW-1K dataset for experimentation. The\ndataset consists of 1000 MWPs and their answers, along with algebraic equa-\ntion templates for solving such problems. The authors used the idea of machine\nlearning introspection and built performance prediction models using random\nforests and XGBoost, and evaluated them on the dataset using ﬁve-fold cross-\nvalidation. ChatGPT’s accuracy increased from an initial 34% to a ﬁnal 69%,\nwhile its recall increased from an initial 41% to a ﬁnal 83%. The authors also","total_pages":35,"creationdate":"2023-04-05T00:33:07+00:00","page":5,"source":"./data/Summary of ChatGPTGPT-4 Research.pdf","trapped":"/False","moddate":"2023-04-05T00:33:07+00:00","creator":"LaTeX with hyperref","producer":"pdfTeX-1.40.21","keywords":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","title":"","page_label":"6","subject":"","author":""}


created_at: 2026-06-01 06:01:44
topic: persistent://default/default/d94e1968-cc22-40f0-8810-2412728b970e
id: 89ad6308-cb37-4b34-a05d-be975b8c29fb
encoding: FLOAT32
metadata:
{"moddate":"2023-04-05T00:33:07+00:00","chroma:document":"while its recall increased from an initial 41% to a ﬁnal 83%. The authors also\nfound that ChatGPT’s failure rate decreased from an initial 84% to a ﬁnal\n20%, indicating that performance can vary greatly depending on speciﬁc job\nrequirements.\nIn the ﬁeld of physics, Lehnert et al. [10] explored the capabilities and limita-\ntions of ChatGPT by studying how it handles obscure physics topics such as the\nswamp land conjecture in string theory. The experimental dialogue began with\nbroader and more general questions in the ﬁeld of string theory before narrowing\ndown to speciﬁc swamp land conjectures and examining ChatGPT’s understand-\ning of them. The study found that ChatGPT could deﬁne and explain diﬀerent\n6","subject":"","trapped":"/False","page_label":"6","total_pages":35,"keywords":"","producer":"pdfTeX-1.40.21","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","page":5,"author":"","creator":"LaTeX with hyperref","title":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","creationdate":"2023-04-05T00:33:07+00:00"}


created_at: 2026-06-01 06:01:44
topic: persistent://default/default/d94e1968-cc22-40f0-8810-2412728b970e
id: 1aac35d0-3bd8-4f3c-9672-130d877ac8fe
encoding: FLOAT32
metadata:
{"title":"","creationdate":"2023-04-05T00:33:07+00:00","trapped":"/False","producer":"pdfTeX-1.40.21","page_label":"7","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","page":6,"keywords":"","author":"","chroma:document":"concepts in various styles, but was not eﬀective in truly connecting various con-\ncepts. It would conﬁdently provide false information and fabricate statements\nwhen necessary, indicating that ChatGPT cannot truly create new knowledge\nor establish new connections. However, in terms of identifying analogies and\ndescribing abstract concepts of visual representation, ChatGPT can cleverly\nuse language. Kortemeyer et al. [11] evaluated ChatGPT’s ability to answer\ncalculus-based physics questions through a question-and-answer test. The tests\nincluded online homework, clicker questions, programming exercises, and exams\ncovering classical mechanics, thermodynamics, electricity and magnetism, and\nmodern physics. While ChatGPT was able to pass the course, it also demon-\nstrated many misconceptions and errors commonly held by beginners. West et\nal. [12] used the Force Concept Inventory (FCI) to evaluate ChatGPT’s accu-\nracy in answering physics concept problems related to kinematics and Newtonian","creator":"LaTeX with hyperref","moddate":"2023-04-05T00:33:07+00:00","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","subject":"","total_pages":35}


created_at: 2026-06-01 06:01:44
topic: persistent://default/default/d94e1968-cc22-40f0-8810-2412728b970e
id: c1ef710c-3471-4805-b087-0872412c0397
encoding: FLOAT32
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{"author":"","chroma:document":"racy in answering physics concept problems related to kinematics and Newtonian\nmechanics in the ﬁrst semester of college physics. The FCI covers topics such\nas kinematics, projectile motion, free fall, circular motion, and Newton’s laws.\nThe study included data from 415 students who took the FCI at the end of the\nsemester, with an average score of 56%, while ChatGPT scored approximately\nbetween 50% to 65%. The authors demonstrated that ChatGPT’s performance\nin physics learning can reach or even exceed the average level of a semester of\ncollege physics.\nIn the medical ﬁeld\nChatGPT’s question-answering capabilities can also be applied in the med-\nical ﬁeld, such as for answering medical questions from patients or assisting\nhealthcare professionals in diagnosing diseases. Nov et al. [13] evaluated the fea-\nsibility of using ChatGPT for patient-doctor communication. The experiment\nextracted 10 representative patient-doctor interactions from EHR, placed the","page_label":"7","trapped":"/False","creator":"LaTeX with hyperref","producer":"pdfTeX-1.40.21","creationdate":"2023-04-05T00:33:07+00:00","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","keywords":"","subject":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","page":6,"moddate":"2023-04-05T00:33:07+00:00","total_pages":35,"title":""}


created_at: 2026-06-01 06:01:44
topic: persistent://default/default/d94e1968-cc22-40f0-8810-2412728b970e
id: 8283e17a-68cf-4ea4-839b-dc757ab30f78
encoding: FLOAT32
metadata:
{"subject":"","moddate":"2023-04-05T00:33:07+00:00","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","creationdate":"2023-04-05T00:33:07+00:00","creator":"LaTeX with hyperref","keywords":"","producer":"pdfTeX-1.40.21","author":"","trapped":"/False","chroma:document":"extracted 10 representative patient-doctor interactions from EHR, placed the\npatient’s questions in ChatGPT, and asked ChatGPT to respond using roughly\nthe same number of words as the doctor’s response. Each patient’s question was\nanswered by either the doctor or ChatGPT, and the patient was informed that\n5 were answered by the doctor and 5 were generated by ChatGPT, and was\nasked to correctly identify the source of the response. The results of the exper-\niment showed that the probability of correctly identifying ChatGPT’s response\nwas 65.5%, while the probability of correctly identifying the doctor’s response\nwas 65.1%. In addition, the experiment found that the patient’s response to\nthe trustworthiness of ChatGPT’s function was weakly positive (average Likert\nscore: 3.4), and trust decreased as the complexity of health-related tasks in\nthe questions increased. ChatGPT’s responses to patient questions were only","page_label":"7","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","total_pages":35,"title":"","page":6}


created_at: 2026-06-01 06:01:44
topic: persistent://default/default/d94e1968-cc22-40f0-8810-2412728b970e
id: 6d350d37-6405-469b-935f-1496b88f7a14
encoding: FLOAT32
metadata:
{"page":6,"title":"","subject":"","creationdate":"2023-04-05T00:33:07+00:00","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","author":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","creator":"LaTeX with hyperref","moddate":"2023-04-05T00:33:07+00:00","keywords":"","chroma:document":"the questions increased. ChatGPT’s responses to patient questions were only\nslightly diﬀerent from those of doctors, but people seem to trust ChatGPT to\nanswer low-risk health questions, while for complex medical questions, people\nstill tend to trust the doctor’s responses and advice.\nTu et al. [14] explored the causal discovery ability of ChatGPT in the diag-\nnosis of neuropathic pain. Causal relationship discovery aims to reveal potential\nunknown causal relationships based purely on observed data [15]. The experi-\nmental results found that ChatGPT has some limitations in understanding new\n7","page_label":"7","producer":"pdfTeX-1.40.21","total_pages":35,"trapped":"/False"}


created_at: 2026-06-01 06:01:44
topic: persistent://default/default/d94e1968-cc22-40f0-8810-2412728b970e
id: 8bc33caa-aa3f-4fec-bad6-22df6a477750
encoding: FLOAT32
metadata:
{"ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","total_pages":35,"producer":"pdfTeX-1.40.21","page":7,"chroma:document":"knowledge and concepts beyond the existing textual training data corpus, that\nis, it only understands language commonly used to describe situations and not\nunderlying knowledge. In addition, its performance consistency and stability are\nnot high, as the experiment observed that it would provide diﬀerent answers for\nthe same question under multiple inquiries. However, despite the many limita-\ntions of ChatGPT, we believe that it has a great opportunity to improve causal\nrelationship research.\nIn the other ﬁeld\nGuo et al. [16] attempted to apply ChatGPT in the ﬁeld of communication,\nspeciﬁcally using ChatGPT for ordered importance semantic communication,\nwhere ChatGPT plays the role of an intelligent consulting assistant that can\nreplace humans in identifying the semantic importance of words in messages and\ncan be directly embedded into the current communication system. For a message\nto be transmitted, the sender ﬁrst utilizes ChatGPT to output the semantic","creationdate":"2023-04-05T00:33:07+00:00","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","keywords":"","title":"","moddate":"2023-04-05T00:33:07+00:00","creator":"LaTeX with hyperref","author":"","page_label":"8","subject":"","trapped":"/False"}


created_at: 2026-06-01 06:01:44
topic: persistent://default/default/d94e1968-cc22-40f0-8810-2412728b970e
id: ac19ba76-11ba-48e2-be42-2ed419e26f60
encoding: FLOAT32
metadata:
{"trapped":"/False","chroma:document":"to be transmitted, the sender ﬁrst utilizes ChatGPT to output the semantic\nimportance order of each word. Then, the transmitter executes an unequal error\nprotection transmission strategy based on the importance order to make the\ntransmission of important words in the message more reliable. The experimental\nresults show that the error rate and semantic loss of important words measured\nin the communication system embedded with ChatGPT are much lower than\nthose of existing communication schemes, indicating that ChatGPT can protect\nimportant words well and make semantic communication more reliable.\nWang et al. [17] studied the eﬀectiveness of ChatGPT in generating high-\nquality Boolean queries for systematic literature search. They designed a wide\nrange of prompts and investigated these tasks on more than 100 systematic\nreview topics. In the end, queries generated by ChatGPT achieved higher accu-\nracy compared to the currently most advanced query generation methods but","keywords":"","creationdate":"2023-04-05T00:33:07+00:00","title":"","page_label":"8","author":"","moddate":"2023-04-05T00:33:07+00:00","creator":"LaTeX with hyperref","page":7,"total_pages":35,"subject":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","producer":"pdfTeX-1.40.21"}


created_at: 2026-06-01 06:01:44
topic: persistent://default/default/d94e1968-cc22-40f0-8810-2412728b970e
id: 20ddf90c-fce8-498f-8821-6e8bed343eb6
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{"source":"./data/Summary of ChatGPTGPT-4 Research.pdf","keywords":"","trapped":"/False","creator":"LaTeX with hyperref","total_pages":35,"page":7,"page_label":"8","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","subject":"","title":"","author":"","producer":"pdfTeX-1.40.21","creationdate":"2023-04-05T00:33:07+00:00","chroma:document":"racy compared to the currently most advanced query generation methods but\nat the cost of reduced recall. For time-limited rapid reviews, it is often accept-\nable to trade oﬀ higher precision for lower recall. Additionally, ChatGPT can\ngenerate high search accuracy Boolean queries by guiding the prompts. How-\never, it should be noted that when two queries use the same prompts, ChatGPT\ngenerates diﬀerent queries, indicating its limitations in consistency and stabil-\nity. Overall, this study demonstrated the potential of ChatGPT in generating\neﬀective Boolean queries for systematic literature searches.\n2.1.2 Text Classiﬁcation\nThe purpose of text classiﬁcation is to assign text data to predeﬁned categories.\nThis task is critical for many applications, including sentiment analysis, spam\ndetection, and topic modeling. While traditional machine learning algorithms\nhave been widely used for text classiﬁcation, recent advances in natural lan-","moddate":"2023-04-05T00:33:07+00:00"}


created_at: 2026-06-01 06:01:44
topic: persistent://default/default/d94e1968-cc22-40f0-8810-2412728b970e
id: 18399ae2-8476-4588-b678-d3ca7e37643f
encoding: FLOAT32
metadata:
{"ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","keywords":"","author":"","chroma:document":"have been widely used for text classiﬁcation, recent advances in natural lan-\nguage processing have led to the development of more advanced techniques.\nChatGPT has shown immense potential in this ﬁeld. Its ability to accurately\nclassify text, ﬂexibility in handling various classiﬁcation tasks, and potential for\ncustomization make it a valuable tool for text classiﬁcation, as evidenced by\nseveral studies in the literature.\n8","page":7,"creator":"LaTeX with hyperref","moddate":"2023-04-05T00:33:07+00:00","page_label":"8","creationdate":"2023-04-05T00:33:07+00:00","title":"","trapped":"/False","producer":"pdfTeX-1.40.21","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","total_pages":35,"subject":""}


created_at: 2026-06-01 06:01:44
topic: persistent://default/default/d94e1968-cc22-40f0-8810-2412728b970e
id: 433cad81-d3b7-44ec-bd43-8ae0f8d4095a
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{"keywords":"","author":"","producer":"pdfTeX-1.40.21","subject":"","total_pages":35,"chroma:document":"Kuzman et al. [18] employed ChatGPT for automatic genre recognition, with\nthe goal of simplifying the text classiﬁcation task by utilizing ChatGPT’s zero-\nshot classiﬁcation capability. They compared ChatGPT’s genre recognition per-\nformance, using two prompt languages (EN and SL), with the X-GENRE clas-\nsiﬁer based on the multilingual model XLM-RoBERTa on the English dataset\nEN-GINCO and the Slovenian dataset GINCO. The results showed that when\nEN was used as the prompt language, ChatGPT achieved Micro F1, Macro F1,\nand Accuracy scores of 0.74, 0.66, and 0.72. However, on the GINCO dataset,\nChatGPT’s genre recognition performance with both EN and SL prompt lan-\nguages was lower than that of the X-GENRE classiﬁer to varying degrees.\nAmin et al. [19] evaluated the text classiﬁcation ability of ChatGPT in\naﬀective computing by using it to perform personality prediction, sentiment\nanalysis, and suicide ideation detection tasks. They prompted ChatGPT with","page_label":"9","page":8,"creator":"LaTeX with hyperref","moddate":"2023-04-05T00:33:07+00:00","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","title":"","trapped":"/False","creationdate":"2023-04-05T00:33:07+00:00"}


created_at: 2026-06-01 06:01:44
topic: persistent://default/default/d94e1968-cc22-40f0-8810-2412728b970e
id: 7c622e32-64d9-426f-bba9-bbfefcae606b
encoding: FLOAT32
metadata:
{"keywords":"","author":"","title":"","creator":"LaTeX with hyperref","total_pages":35,"ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","page":8,"page_label":"9","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","producer":"pdfTeX-1.40.21","moddate":"2023-04-05T00:33:07+00:00","chroma:document":"analysis, and suicide ideation detection tasks. They prompted ChatGPT with\ncorresponding prompts on three datasets: First Impressions, Sentiment140, and\nSuicide and Depression, and compared its classiﬁcation performance with three\nbaseline models: RoBERTa-base, Word2Vec, and BoW. The results showed\nthat ChatGPT’s accuracy and UAR for the ﬁve personality classiﬁcations on\nthe First Impressions dataset were lower than the baseline methods to varying\ndegrees. On the Sentiment140 dataset, ChatGPT’s accuracy and UAR were 85.5\nand 85.5, respectively, which were better than the three baseline methods. On\nthe Suicide and Depression dataset, ChatGPT’s accuracy and UAR were 92.7\nand 91.2, respectively, which were lower than RoBERTa, the best-performing\nbaseline method.\nZhang et al. [20] employed ChatGPT for stance detection, which includes\nsupport and opposition. They used ChatGPT to classify the political stance\nof tweets in the SemEval-2016 and P-Stance datasets. SemEval-2016 contains","subject":"","creationdate":"2023-04-05T00:33:07+00:00","trapped":"/False"}


created_at: 2026-06-01 06:01:44
topic: persistent://default/default/d94e1968-cc22-40f0-8810-2412728b970e
id: aa178c73-3268-4ef7-98c0-a761ac792c14
encoding: FLOAT32
metadata:
{"page":8,"source":"./data/Summary of ChatGPTGPT-4 Research.pdf","subject":"","keywords":"","author":"","producer":"pdfTeX-1.40.21","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","creator":"LaTeX with hyperref","trapped":"/False","total_pages":35,"chroma:document":"of tweets in the SemEval-2016 and P-Stance datasets. SemEval-2016 contains\n4,870 English tweets, and they selected tweets with the most commonly occur-\nring FM, LA, and HC political labels for stance classiﬁcation. The P-Stance\ndataset has 21,574 English tweets, and they classiﬁed the stance of tweets to-\nwards Trump, Biden, and Bernie. The ﬁnal results showed that on the SemEval-\n2016 dataset, ChatGPT achieved F1-m scores of 68.4, 58.2, and 79.5 for the FM,\nLA, and HC political labels, and F1-avg scores of 72.6, 59.3, and 78.0, respec-\ntively. On the P-Stance dataset, ChatGPT achieved F1-m scores of 82.8, 82.3,\nand 79.4 for the Trump, Biden, and Bernie political ﬁgures, and F1-avg scores\nof 83.2, 82.0, and 79.4, respectively.\nHuang et al. [21] used ChatGPT to detect implicit hate speech in tweets.\nThey selected 12.5% (795 tweets) of the LatentHatred dataset containing im-\nplicit hate speech and asked ChatGPT to classify them into three categories:","page_label":"9","title":"","moddate":"2023-04-05T00:33:07+00:00","creationdate":"2023-04-05T00:33:07+00:00"}


created_at: 2026-06-01 06:01:44
topic: persistent://default/default/d94e1968-cc22-40f0-8810-2412728b970e
id: 5df65a80-9fc5-4b58-afc4-19ea53a66b97
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{"page_label":"9","producer":"pdfTeX-1.40.21","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","author":"","trapped":"/False","creator":"LaTeX with hyperref","keywords":"","subject":"","creationdate":"2023-04-05T00:33:07+00:00","chroma:document":"plicit hate speech and asked ChatGPT to classify them into three categories:\nimplicit hate speech, non-hate speech, and uncertain. The results showed that\nChatGPT correctly recognized 636 (80%) of the tweets. The number of tweets\nclassiﬁed as non-hate speech and uncertain were 146 (18.4%) and 13 (1.6%),\nrespectively. The results of the reclassiﬁcation of tweets in the non-hate speech\nand uncertain categories by Amazon Mechanical Turk (Mturk) workers were\nconsistent with ChatGPT’s classiﬁcation.\nOverall, ChatGPT has tremendous potential in text classiﬁcation tasks, as it\ncan eﬀectively address problems such as genre identiﬁcation, sentiment analysis,\n9","total_pages":35,"page":8,"moddate":"2023-04-05T00:33:07+00:00","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","title":""}


created_at: 2026-06-01 06:01:44
topic: persistent://default/default/d94e1968-cc22-40f0-8810-2412728b970e
id: 00385fff-991c-4ba9-ae64-768b1be671f0
encoding: FLOAT32
metadata:
{"page":9,"keywords":"","title":"","trapped":"/False","page_label":"10","chroma:document":"stance detection, and more. However, there are still challenges that ChatGPT\nfaces in the ﬁeld of text classiﬁcation. Firstly, it struggles to perform well in\nclassiﬁcation tasks with rare or out-of-vocabulary words since it heavily relies\non the distribution of training data. Additionally, the signiﬁcant computational\nresources required for training and utilizing ChatGPT can limit its use in some\napplications.\n2.1.3 Text generation\nWe live in an era of information explosion, and text is an eﬃcient way of trans-\nmitting information. The diversity of information has led to a diversity of text\ncategories. When researchers use ChatGPT’s text generation capabilities for\nresearch, they inevitably choose to generate diﬀerent types of text. In the pro-\ncess of reading papers, we found that the word count of the text generated by\nresearchers increased from small to large, so we wanted to summarize existing\nresearch based on the size of the text word count. We divided the generated","total_pages":35,"producer":"pdfTeX-1.40.21","moddate":"2023-04-05T00:33:07+00:00","creationdate":"2023-04-05T00:33:07+00:00","author":"","subject":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","creator":"LaTeX with hyperref","source":"./data/Summary of ChatGPTGPT-4 Research.pdf"}


created_at: 2026-06-01 06:01:44
topic: persistent://default/default/d94e1968-cc22-40f0-8810-2412728b970e
id: 9ae7260b-d97c-4739-bfae-7c2387e4a3b9
encoding: FLOAT32
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{"subject":"","producer":"pdfTeX-1.40.21","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","chroma:document":"research based on the size of the text word count. We divided the generated\ntext into three levels: phrases, sentences, and paragraphs.\nThe following article uses ChatGPT to generate phrases. Zhang et al. [22]\nproves that the semantic HAR model with semantic augmentation added dur-\ning training performs better in motion recognition than other models. Semantic\naugmentation requires shared tokens, which is lacking in some datasets. There-\nfore, authors leverage ChatGPT for an automated label generation approach\nfor datasets originally without shared tokens. Fu et al. [23] describes a new\nworkﬂow for converting natural language commands into Bash commands. The\nauthor uses ChatGPT to generate a candidate list of Bash commands based\non user input, and then uses a combination of heuristic and machine learning\ntechniques to rank and select the most likely candidates. This workﬂow was\nevaluated on a real command dataset and achieved high accuracy compared","moddate":"2023-04-05T00:33:07+00:00","creator":"LaTeX with hyperref","trapped":"/False","creationdate":"2023-04-05T00:33:07+00:00","author":"","page_label":"10","title":"","page":9,"keywords":"","total_pages":35,"source":"./data/Summary of ChatGPTGPT-4 Research.pdf"}


created_at: 2026-06-01 06:01:44
topic: persistent://default/default/d94e1968-cc22-40f0-8810-2412728b970e
id: 019b6733-47c5-45dd-ab74-baeb068c3d8f
encoding: FLOAT32
metadata:
{"creator":"LaTeX with hyperref","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","moddate":"2023-04-05T00:33:07+00:00","subject":"","page":9,"title":"","producer":"pdfTeX-1.40.21","total_pages":35,"keywords":"","creationdate":"2023-04-05T00:33:07+00:00","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","author":"","page_label":"10","trapped":"/False","chroma:document":"evaluated on a real command dataset and achieved high accuracy compared\nto other state-of-the-art methods. Chen et al. [24] used the Bart model and\nChatGPT for the task of summarizing humorous titles and compared the perfor-\nmance of the two models. It was found that the Bart model performed better on\nlarge datasets, but ChatGPT was competitive with our best ﬁne-tuned model\nin a small range (48), albeit slightly weaker.\nThe following article uses ChatGPT to generate sentences.Chen et al. [25]\nconstructed a dialogue dataset (HPD) with scenes, timelines, character at-\ntributes, and character relationships in order to use ChatGPT as a conver-\nsational agent to generate dialogue. However, ChatGPT’s performance on the\ntest set was poor, and there is room for improvement.In study [26], chatGPT\ndemonstrated its ability to simplify complex text by providing three ﬁctional\nradiology reports to chatGPT for simpliﬁcation. Most radiologists found the"}


created_at: 2026-06-01 06:01:44
topic: persistent://default/default/d94e1968-cc22-40f0-8810-2412728b970e
id: 3a73b2cc-0f13-45de-b02e-26b86f291c19
encoding: FLOAT32
metadata:
{"producer":"pdfTeX-1.40.21","page_label":"10","moddate":"2023-04-05T00:33:07+00:00","creator":"LaTeX with hyperref","page":9,"trapped":"/False","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","keywords":"","subject":"","creationdate":"2023-04-05T00:33:07+00:00","chroma:document":"radiology reports to chatGPT for simpliﬁcation. Most radiologists found the\nsimpliﬁed reports to be accurate and complete, with no potential harm to pa-\ntients. However, some errors, omissions of critical medical information and text\npassages were identiﬁed, which could potentially lead to harmful conclusions if\nnot understood by the physicians. Xia et al. [27] proposes a new program re-\npair paradigm called Session-based Automatic Program Repair (APR). In APR,\nthe previously generated patches are iteratively built upon by combining them\n10","author":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","total_pages":35,"title":""}


created_at: 2026-06-01 06:01:44
topic: persistent://default/default/d94e1968-cc22-40f0-8810-2412728b970e
id: db5e507e-9801-4e4a-923a-ffedd45e239e
encoding: FLOAT32
metadata:
{"keywords":"","title":"","subject":"","page":10,"total_pages":35,"moddate":"2023-04-05T00:33:07+00:00","trapped":"/False","author":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","creator":"LaTeX with hyperref","producer":"pdfTeX-1.40.21","page_label":"11","chroma:document":"with validation feedback to construct the model’s input. The eﬀectiveness of\nthe approach is veriﬁed using the QuixBugs dataset. The experiment shows\nthat ChatGPT ﬁne-tuned with reinforcement learning from human feedback\n(RLHF) outperforms Codex trained unsupervisedly in both repair datasets. In\nreference to study [28], ChatGPT was compared to three commercial transla-\ntion products: Google Translate2, DeepL Translate3, and Tencent TranSmart4.\nThe evaluation was conducted on the Flores101 test set, using the WMT19\nbiomedical translation task to test translation robustness, with BLEU score as\nthe main metric. The study found that ChatGPT is competitive with commer-\ncial translation products on high-resource European languages but falls behind\non low-resource or distant languages. The authors explored an interesting strat-\negy called pivot prompts, which signiﬁcantly improved translation performance.\nWhile ChatGPT did not perform as well as commercial systems on biomedical","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","creationdate":"2023-04-05T00:33:07+00:00"}


created_at: 2026-06-01 06:01:44
topic: persistent://default/default/d94e1968-cc22-40f0-8810-2412728b970e
id: 45f98cea-7391-42e8-8bca-6c3e21b54952
encoding: FLOAT32
metadata:
{"source":"./data/Summary of ChatGPTGPT-4 Research.pdf","producer":"pdfTeX-1.40.21","subject":"","author":"","title":"","page":10,"chroma:document":"While ChatGPT did not perform as well as commercial systems on biomedical\nabstracts or Reddit comments, it may be a good speech translator. Prieto et\nal. [29] evaluated the use of ChatGPT in developing an automated construction\nschedule based on natural language prompts. The experiment required building\nnew partitions in an existing space and providing details on the rooms to be\npartitioned. The results showed that ChatGPT was able to generate a coher-\nent schedule that followed a logical approach to meet the requirements of the\ngiven scope. However, there were still several major ﬂaws that would limit the\nuse of this tool in real-world projects.Michail et al. [30] proposed a method\nto improve the prediction accuracy of the HeFit ﬁne-tuned XLM T model on\ntweet intimacy by generating a dataset of tweets with intimacy rating tags using\nChatGPT. The speciﬁc operation is to input tweets with intimacy rating tags\ninto ChatGPT and then output similar tweets.","page_label":"11","trapped":"/False","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","creator":"LaTeX with hyperref","creationdate":"2023-04-05T00:33:07+00:00","keywords":"","total_pages":35,"moddate":"2023-04-05T00:33:07+00:00"}


created_at: 2026-06-01 06:01:44
topic: persistent://default/default/d94e1968-cc22-40f0-8810-2412728b970e
id: 01c10b56-ab5e-4650-b0b2-7fb80551ba49
encoding: FLOAT32
metadata:
{"ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","total_pages":35,"title":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","page_label":"11","producer":"pdfTeX-1.40.21","subject":"","creator":"LaTeX with hyperref","trapped":"/False","page":10,"chroma:document":"into ChatGPT and then output similar tweets.\nThe following article uses ChatGPT to generate paragraphs. Wang et al.\n[31] compared the abstract summarization performance of ChatGPT and other\nmodels on various cross-lingual text datasets and found that ChatGPT may\nperform worse in metrics such as R 1, R 2, R L, and B S. Yang et al. [32]\nsummarized the performance of ChatGPT in question answering-based text\nsummarization and found that, compared to ﬁne-tuned models, ChatGPT’s\nperformance is slightly worse in all performance metrics. However, the article\nsuggests that if the dataset is golden annotation, ChatGPT’s performance may\nsurpass ﬁne-tuned models in these metrics. Belouadi et al. [33] compared the\nability of ByGPT5 and ChatGPT trained on a range of labeled and unlabeled\ndatasets of English and German poetry to generate constrained style poetry, and\nevaluated them using three metrics: Rhyme, ScoreAlliteration, and ScoreMe-","creationdate":"2023-04-05T00:33:07+00:00","author":"","keywords":"","moddate":"2023-04-05T00:33:07+00:00"}


created_at: 2026-06-01 06:01:44
topic: persistent://default/default/d94e1968-cc22-40f0-8810-2412728b970e
id: 9dbea89d-7dd5-4b32-9184-3ab998ecc323
encoding: FLOAT32
metadata:
{"total_pages":35,"creationdate":"2023-04-05T00:33:07+00:00","page_label":"11","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","page":10,"keywords":"","producer":"pdfTeX-1.40.21","author":"","moddate":"2023-04-05T00:33:07+00:00","chroma:document":"evaluated them using three metrics: Rhyme, ScoreAlliteration, and ScoreMe-\nter Score. The conclusion is that ByGPT5 performs better than ChatGPT.\nBlanco-Gonzalez et al. [34] evaluated chatGPT’s ability to write commentary\narticles, and in fact, this article itself was written by chatGPT. The human au-\nthor rewrote the manuscript based on chatGPT’s draft. Experts found that it\ncan quickly generate and optimize text, as well as help users complete multiple\ntasks. However, in terms of generating new content, it is not ideal. Ultimately,\nit can be said that without strong human intervention, chatGPT is not a use-\nful tool for writing reliable scientiﬁc texts. It lacks the knowledge and expertise\nrequired to accurately and fully convey complex scientiﬁc concepts and informa-\n11","trapped":"/False","creator":"LaTeX with hyperref","subject":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","title":""}


created_at: 2026-06-01 06:01:44
topic: persistent://default/default/d94e1968-cc22-40f0-8810-2412728b970e
id: 9349eb90-a3ff-44dd-89e5-f687a373881d
encoding: FLOAT32
metadata:
{"producer":"pdfTeX-1.40.21","page_label":"12","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","moddate":"2023-04-05T00:33:07+00:00","title":"","creator":"LaTeX with hyperref","chroma:document":"tion. Khalil et al. [35] on the originality of content generated by ChatGPT. To\nevaluate the originality of 50 papers on various topics generated by ChatGPT,\ntwo popular plagiarism detection tools, Turnitin and iThenticate, were used.\nThe results showed that ChatGPT has great potential in generating complex\ntext output that is not easily captured by plagiarism detection software. The\nexisting plagiarism detection software should update their plagiarism detection\nengines. Basic et al. [36] conducted a comparison of the writing performance\nof students using or not using ChatGPT-3 as a writing aid. The experiment\nconsisted of two groups of 9 participants each. The control group wrote articles\nusing traditional methods, while the experimental group used ChatGPT as an\naid. Two teachers evaluated the papers. The study showed that the assistance of\nChatGPT did not necessarily improve the quality of the students’ essays.Noever","trapped":"/False","subject":"","total_pages":35,"page":11,"creationdate":"2023-04-05T00:33:07+00:00","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","author":"","keywords":""}


created_at: 2026-06-01 06:01:44
topic: persistent://default/default/d94e1968-cc22-40f0-8810-2412728b970e
id: d2ecd1ec-de79-42b1-a2ec-c8c8437f738a
encoding: FLOAT32
metadata:
{"moddate":"2023-04-05T00:33:07+00:00","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","subject":"","total_pages":35,"page":11,"creator":"LaTeX with hyperref","author":"","keywords":"","chroma:document":"ChatGPT did not necessarily improve the quality of the students’ essays.Noever\net al. [37] discusses the potential of using artiﬁcial intelligence (AI), particularly\nlanguage models like GPT (including GPT-3), to create more convincing chat-\nbots that can deceive humans into thinking they are interacting with another\nperson. The article describes a series of experiments in which they used GPT-3\nto generate chatbot responses that mimic human-like conversations and were\ntested on human participants. The results show that some participants were\nunable to distinguish between the chatbot and a real human, highlighting the\npotential for these AI chatbots to be used for deceptive purposes.\n2.1.4 Code Generation\nCode generation refers to the process of automatically generating computer code\nfrom high-level descriptions or speciﬁcations. ChatGPT’s advanced natural lan-\nguage processing capabilities make it capable of performing code generation","page_label":"12","producer":"pdfTeX-1.40.21","creationdate":"2023-04-05T00:33:07+00:00","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","title":"","trapped":"/False"}


created_at: 2026-06-01 06:01:44
topic: persistent://default/default/d94e1968-cc22-40f0-8810-2412728b970e
id: 0746f40a-9988-4de1-98b7-8c7b5534539f
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metadata:
{"keywords":"","creator":"LaTeX with hyperref","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","total_pages":35,"producer":"pdfTeX-1.40.21","page":11,"page_label":"12","subject":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","trapped":"/False","author":"","chroma:document":"guage processing capabilities make it capable of performing code generation\ntasks. By analyzing the requirements for code generation, ChatGPT can pro-\nduce code snippets that accurately execute the intended functionality. This not\nonly saves time and eﬀort in writing code from scratch but also reduces the risk\nof errors that may occur during manual coding. In addition, ChatGPT’s ability\nto learn and adapt to new programming languages and frameworks enables it\nto complete more complex programming tasks. For example:\nMegahed et al. [38] discussed the potential of using ChatGPT for tasks\nsuch as code explanation, suggesting alternative methods for problem-solving\nwith code, and translating code between programming languages. The solutions\nprovided by ChatGPT were found to be viable. In another study, Treude et\nal. [39] introduced a ChatGPT-based prototype called GPTCOMCARE, which\nhelps programmers generate multiple solutions for a programming problem and","moddate":"2023-04-05T00:33:07+00:00","title":"","creationdate":"2023-04-05T00:33:07+00:00"}


created_at: 2026-06-01 06:01:44
topic: persistent://default/default/d94e1968-cc22-40f0-8810-2412728b970e
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{"title":"","chroma:document":"helps programmers generate multiple solutions for a programming problem and\nhighlight the diﬀerences between each solution using colors.Sobania et al. [40]\nutilized ChatGPT for code bug ﬁxing, and further improved the success rate of\nbug ﬁxing by inputting more information through its dialogue system. Speciﬁ-\ncally, the QuixBugs standard bug ﬁxing benchmark contained 40 code bugs that\nneeded to be ﬁxed. With limited information, ChatGPT ﬁxed 19 bugs, which\nwas slightly lower than the 21 bugs ﬁxed by the Codex model, but signiﬁcantly\nhigher than the 7 ﬁxed by the Standard APR model. When given more prompts\nand information, ChatGPT was able to ﬁx 31 bugs, demonstrating its potential\n12","subject":"","author":"","keywords":"","creationdate":"2023-04-05T00:33:07+00:00","moddate":"2023-04-05T00:33:07+00:00","total_pages":35,"page_label":"12","trapped":"/False","page":11,"ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","producer":"pdfTeX-1.40.21","creator":"LaTeX with hyperref"}


created_at: 2026-06-01 06:01:44
topic: persistent://default/default/d94e1968-cc22-40f0-8810-2412728b970e
id: bf0a526f-9459-4334-b84d-3b492d348904
encoding: FLOAT32
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{"ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","trapped":"/False","author":"","page_label":"13","producer":"pdfTeX-1.40.21","creator":"LaTeX with hyperref","title":"","creationdate":"2023-04-05T00:33:07+00:00","chroma:document":"for code bug ﬁxing tasks.Xia et al. [27] proposed a conversational approach\nfor Automated Program Repair (APR), which alternates between generating\npatches and validating them against feedback from test cases until the correct\npatch is generated. Selecting 30 bugs from the QuixBugs standard bug ﬁxing\nbenchmark, which are suitable for test case feedback, and demonstrating them\nwith Java and Python, the QuixBugs-Python and QuixBugs-Java datasets were\nobtained. The conversational APR using ChatGPT outperformed the conversa-\ntional APR using Codex and the conversational APR using CODEGEN (with\nmodel parameters of 350M, 2B, 6B, and 16B) on both datasets. Furthermore,\nChatGPT’s conversational APR generated and validated patches with signiﬁ-\ncantly fewer feedback loops than the other models.\nChatGPT can not only be used to achieve some simple code generation tasks\nbut also can be used to accomplish some complex programming tasks. Noever","subject":"","moddate":"2023-04-05T00:33:07+00:00","total_pages":35,"page":12,"keywords":""}


created_at: 2026-06-01 06:01:44
topic: persistent://default/default/d94e1968-cc22-40f0-8810-2412728b970e
id: 13873258-354a-4802-b07d-6779bb5effe9
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{"title":"","moddate":"2023-04-05T00:33:07+00:00","subject":"","page":12,"trapped":"/False","chroma:document":"but also can be used to accomplish some complex programming tasks. Noever\net al. [41] tested ChatGPT’s code generation capabilities on four datasets -\nIris, Titanic, Boston Housing, and Faker. When prompted to mimic a Python\ninterpreter in the form of a Jupyter notebook, the model was able to generate\nindependent code based on the prompt and respond with the expected out-\nput. For example, when given the prompt ”data.cor()” for the Iris dataset,\nChatGPT generated correct Python output. The test results indicate that\nChatGPT can access structured datasets and perform basic software opera-\ntions required by databases, such as create, read, update, and delete (CRUD).\nThis suggests that cutting-edge language models like ChatGPT have the nec-\nessary scale to tackle complex problems. McKee et al. [42] utilized ChatGPT\nas an experimental platform to investigate cybersecurity issues. They modeled\nﬁve diﬀerent modes of computer virus properties, including self-replication, self-","creationdate":"2023-04-05T00:33:07+00:00","producer":"pdfTeX-1.40.21","total_pages":35,"page_label":"13","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","author":"","keywords":"","creator":"LaTeX with hyperref","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2"}


created_at: 2026-06-01 06:01:44
topic: persistent://default/default/d94e1968-cc22-40f0-8810-2412728b970e
id: 522e48f4-05dc-4d04-a568-9b2ca8a3a360
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{"moddate":"2023-04-05T00:33:07+00:00","subject":"","keywords":"","trapped":"/False","page":12,"author":"","page_label":"13","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","creationdate":"2023-04-05T00:33:07+00:00","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","creator":"LaTeX with hyperref","title":"","producer":"pdfTeX-1.40.21","total_pages":35,"chroma:document":"ﬁve diﬀerent modes of computer virus properties, including self-replication, self-\nmodiﬁcation, execution, evasion, and application, using ChatGPT. These ﬁve\nmodes encompassed thirteen encoding tasks from credential access to defense\nevasion within the MITRE ATT&CK framework. The results showed that the\nquality of ChatGPT’s generated code was generally above average, except for the\nself-replication mode, where it performed poorly.They [43] also employed Chat-\nGPT as a network honeypot to defend against attackers. By having ChatGPT\nmimic Linux, Mac, and Windows terminal commands and providing interfaces\nfor TeamViewer, nmap, and ping, a dynamic environment can be created to\nadapt to attackers’ operations, and logs can be used to gain insight into their\nattack methods, tactics, and procedures. The authors demonstrated ten hon-\neypot tasks to illustrate that ChatGPT’s interface not only provides suﬃcient\nAPI memory to execute previous commands without defaulting to repetitive"}


created_at: 2026-06-01 06:01:44
topic: persistent://default/default/d94e1968-cc22-40f0-8810-2412728b970e
id: c55f92df-a046-4064-80f4-254c6c977f2e
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{"moddate":"2023-04-05T00:33:07+00:00","page":12,"keywords":"","creator":"LaTeX with hyperref","producer":"pdfTeX-1.40.21","subject":"","trapped":"/False","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","total_pages":35,"title":"","page_label":"13","author":"","creationdate":"2023-04-05T00:33:07+00:00","chroma:document":"API memory to execute previous commands without defaulting to repetitive\nintroductory tasks but also oﬀers a responsive welcome program that maintains\nattackers’ interest in multiple queries.\nIn the ﬁeld of code generation, there are still several challenges with Chat-\nGPT. Firstly, its application scope is limited as its training data is biased to-\nwards programming languages such as Python, C++, and Java, making it po-\ntentially unsuitable for some programming languages or coding styles. Secondly,\nmanual optimization is necessary for code formatting, as the generated code may\nnot be performance-optimized or follow best coding practices, requiring manual\nediting and optimization. Lastly, the quality of the generated code cannot be\n13"}


created_at: 2026-06-01 06:01:44
topic: persistent://default/default/d94e1968-cc22-40f0-8810-2412728b970e
id: 49234a53-f159-4530-9165-2283bc34a323
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{"author":"","creationdate":"2023-04-05T00:33:07+00:00","page":13,"chroma:document":"guaranteed, as it heavily relies on the quality of the natural language input,\nwhich may contain errors, ambiguities, or inconsistencies, ultimately aﬀecting\nthe accuracy and reliability of the generated code.\n2.1.5 Inference\nInference refers to the process of drawing new conclusions or information through\nlogical deduction from known facts or information. It is typically based on a se-\nries of premises or assumptions, and involves applying logical rules or reasoning\nmethods to arrive at a conclusion. Inference is an important ability in human\nthinking, and is often used to solve problems, make decisions, analyze and eval-\nuate information, etc. Inference also plays a key role in ﬁelds such as science,\nphilosophy, law, etc. There are two types of inference: inductive reasoning,\nwhich involves deriving general rules or conclusions from known facts or expe-\nriences, and deductive reasoning, which involves deriving speciﬁc conclusions","keywords":"","producer":"pdfTeX-1.40.21","trapped":"/False","page_label":"14","creator":"LaTeX with hyperref","subject":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","total_pages":35,"moddate":"2023-04-05T00:33:07+00:00","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","title":""}


created_at: 2026-06-01 06:01:44
topic: persistent://default/default/d94e1968-cc22-40f0-8810-2412728b970e
id: 55e8502a-d6eb-40c5-822c-4ad6e81b85da
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{"producer":"pdfTeX-1.40.21","trapped":"/False","subject":"","moddate":"2023-04-05T00:33:07+00:00","creator":"LaTeX with hyperref","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","author":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","page":13,"total_pages":35,"page_label":"14","title":"","keywords":"","chroma:document":"riences, and deductive reasoning, which involves deriving speciﬁc conclusions\nfrom known premises or assumptions. Whether inductive or deductive, the pro-\ncess of inference requires following strict logical rules to ensure the correctness\nand reliability of the inference.\nSome papers attempt to use ChatGPT’s ability in inductive reasoning to\ncapture the meaning in text and use deﬁned metrics to score the text. Michail\net al. [30] uses ChatGPT to infer intimacy expressed in tweets. They ﬁrst input\n50 tweets with intimacy markers to ChatGPT, then use inductive reasoning to\ninfer the standards for generating tweets with diﬀerent levels of intimacy, and\nﬁnally generate ten tweets with intimacy values ranging from 0 to 5. Susnjak\net al. [44] collected a large amount of textual data from patient-doctor dis-\ncussion forums, patient testimonials, social media platforms, medical journals,\nand other scientiﬁc research publications. Using the BERT model, the author","creationdate":"2023-04-05T00:33:07+00:00"}


created_at: 2026-06-01 06:01:44
topic: persistent://default/default/d94e1968-cc22-40f0-8810-2412728b970e
id: d0ef5768-d32e-4d37-873e-85cda24e0603
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{"title":"","trapped":"/False","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","keywords":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","page":13,"producer":"pdfTeX-1.40.21","moddate":"2023-04-05T00:33:07+00:00","page_label":"14","subject":"","chroma:document":"and other scientiﬁc research publications. Using the BERT model, the author\ninferred emotion values from 0 to 1. The author visualized the process of how\nthe presence of bias in the discourse surrounding chronic manifestations of the\ndisease using the SHAP tool. The author also envisioned ChatGPT as a replace-\nment for the BERT model for scoring the emotional value of text. Huang et al.\n[21] chose 12.5% of individuals in the potential hate dataset as study materials,\ninduced ChatGPT to make classiﬁcations based on a prompt, and ChatGPT\nproduced three classiﬁcations: unclear, yes, and no. The author assigned a\nvalue of 1 to yes, -1 to no, and 0 to unclear, and had ChatGPT score and clas-\nsify them. ChatGPT was able to correctly classify 80% of implicit hate tweets\nin the author’s experimental setup, demonstrating ChatGPT’s great potential\nas a data labeling tool using simple prompts.\nSome papers have evaluated ChatGPT’s reasoning performance, mainly in","author":"","creator":"LaTeX with hyperref","creationdate":"2023-04-05T00:33:07+00:00","total_pages":35}


created_at: 2026-06-01 06:01:44
topic: persistent://default/default/d94e1968-cc22-40f0-8810-2412728b970e
id: b4e1401e-aa90-4bb9-9cf4-07445f43c59f
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{"author":"","subject":"","page_label":"14","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","producer":"pdfTeX-1.40.21","trapped":"/False","total_pages":35,"creator":"LaTeX with hyperref","page":13,"keywords":"","chroma:document":"Some papers have evaluated ChatGPT’s reasoning performance, mainly in\ndecision-making and spatial reasoning, and identifying ambiguity. Tang et al.\n[45] used the independence axiom and the transitivity axiom, as well as other\nnon-VNM related decision-making abilities, by presenting bets conditioned on\nrandom events, bets with asymmetric outcomes, decisions encapsulating Sav-\nage’s Sure Thing principle, and other complex bet structures like nested bets, to\ndesign experiments where each experiment input a short prompt to ChatGPT\nand evaluated the results. The conclusion is that ChatGPT exhibits uncer-\n14","moddate":"2023-04-05T00:33:07+00:00","title":"","creationdate":"2023-04-05T00:33:07+00:00","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2"}


created_at: 2026-06-01 06:01:44
topic: persistent://default/default/d94e1968-cc22-40f0-8810-2412728b970e
id: ea41121e-f4a9-4757-aa42-c0069be26f8c
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{"author":"","creationdate":"2023-04-05T00:33:07+00:00","creator":"LaTeX with hyperref","page":14,"moddate":"2023-04-05T00:33:07+00:00","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","producer":"pdfTeX-1.40.21","trapped":"/False","chroma:document":"tainty in the decision-making process: in some cases, large language models\ncan arrive at the correct answer through incorrect reasoning; and it may make\nsuboptimal decisions for simple reasoning problems. Ortega-Martn et al. [46]\nhad ChatGPT detect three diﬀerent levels of language ambiguity and evaluated\nits performance. The conclusion is that In semantics, ChatGPT performed per-\nfectly in the detection of ambiguities. Apart from that, it has some bright sports\n(co-reference resolution) and some weaknesses (puts gender bias over grammar\nin some non-ambiguous situations). In the generation task ChatGPT did well,\nbut also revealed some of its worse issues: the lack of systematicity. Lastly, it\nshould also be pointed that in most of the cases ChatGPT brilliantly alludes to\nlack of context as the key factor in disambiguation.\n2.1.6 Data or information extraction, transformation, enhancement,\nprocessing\nData Visualization","total_pages":35,"subject":"","keywords":"","page_label":"15","title":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2"}


created_at: 2026-06-01 06:01:44
topic: persistent://default/default/d94e1968-cc22-40f0-8810-2412728b970e
id: e886f283-050d-4c96-9ec0-05aafd05dc3e
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{"moddate":"2023-04-05T00:33:07+00:00","creator":"LaTeX with hyperref","keywords":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","producer":"pdfTeX-1.40.21","page":14,"title":"","creationdate":"2023-04-05T00:33:07+00:00","chroma:document":"2.1.6 Data or information extraction, transformation, enhancement,\nprocessing\nData Visualization\nNatural language interfaces have contributed to generating visualizations\ndirectly from natural language, but visualization problems remain challenging\ndue to the ambiguity of natural language.ChatGPT provides a new avenue for\nthe ﬁeld by converting natural language into visualized code.\nIn terms of data visualization, Noever et al. [41] tested ChatGPT’s ba-\nsic arithmetic skills by asking questions.On the iris dataset, Titanic survival\ndataset, Boston housing data, and randomly generated insurance claims dataset,\nthe statistical analysis of data and visualization problems were converted to\nprogramming problems using Jupyter to verify ChatGPT’s ability to generate\npython code to draw suitable graphs and analyze the data. The results show\nthat ChatGPT can access structured and organized datasets to perform the\nfour basic software operations required for databases: create, read, update, and","total_pages":35,"author":"","subject":"","trapped":"/False","page_label":"15","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2"}


created_at: 2026-06-01 06:01:44
topic: persistent://default/default/d94e1968-cc22-40f0-8810-2412728b970e
id: 909c382f-c988-4010-a8e2-7c9f10f8ae29
encoding: FLOAT32
metadata:
{"moddate":"2023-04-05T00:33:07+00:00","page_label":"15","producer":"pdfTeX-1.40.21","page":14,"title":"","total_pages":35,"ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","chroma:document":"four basic software operations required for databases: create, read, update, and\ndelete, and generate suitable python code to plot graphs for descriptive statis-\ntics, variable correlation analysis, describing trends, and other data analysis\noperations.Maddigan et al. [47] proposed an end-to-end solution for visualizing\ndata in natural language using LLM, which uses an open-source python frame-\nwork designed to generate appropriate hints for selected datasets to make LLM\nmore eﬀective in understanding natural language, and uses internal reasoning\ncapabilities to select the appropriate visualization type to generate the code for\nvisualization. In this paper,the reseachers compare the visualization results of\nGPT-3, Codex and ChatGPT in the case of nvBench SQLite database [48] and\nthe visualization results of energy production dataset in the study of ADVISor\nwith NL4DV [49, 50].In addition to, they explore the ability to reason and hy-","creationdate":"2023-04-05T00:33:07+00:00","trapped":"/False","creator":"LaTeX with hyperref","author":"","subject":"","keywords":""}


created_at: 2026-06-01 06:01:44
topic: persistent://default/default/d94e1968-cc22-40f0-8810-2412728b970e
id: 400dc8f2-523f-46f2-8a96-c64110c2e1b7
encoding: FLOAT32
metadata:
{"source":"./data/Summary of ChatGPTGPT-4 Research.pdf","page_label":"15","trapped":"/False","producer":"pdfTeX-1.40.21","creator":"LaTeX with hyperref","subject":"","page":14,"author":"","title":"","chroma:document":"with NL4DV [49, 50].In addition to, they explore the ability to reason and hy-\npothesize of the LLM on movie dataset [48] when the hints are insuﬃcient or\nwrong .Experimental results show that LLM can eﬀectively support the end-to-\nend generation of visualization results from natural language when supported\nby hints, providing an eﬃcient, reliable and accurate solution to the natural\nlanguage visualization problem.\n15","moddate":"2023-04-05T00:33:07+00:00","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","total_pages":35,"keywords":"","creationdate":"2023-04-05T00:33:07+00:00"}


created_at: 2026-06-01 06:01:44
topic: persistent://default/default/d94e1968-cc22-40f0-8810-2412728b970e
id: 33284336-a89e-4168-a21f-b0b761ec9d94
encoding: FLOAT32
metadata:
{"subject":"","producer":"pdfTeX-1.40.21","keywords":"","total_pages":35,"author":"","page_label":"16","creationdate":"2023-04-05T00:33:07+00:00","page":15,"ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","title":"","moddate":"2023-04-05T00:33:07+00:00","chroma:document":"Information Extraction\nThe goal of information extraction is to extract speciﬁc information from\nnatural language text for structured representation, including three important\nsubtasks such as entity relationship extraction, named entity recognition, and\nevent extraction, which have wide applications in business, medical, and other\nﬁelds.\nIn information extraction, Wei et al. [51] proposed ChatIE, a ChatGPT-\nbased multi-round question-and-answer framework for information extraction.\nThe framework decomposes a complex information extraction (IE) task into\nseveral parts, then combines the results of each round into a ﬁnal structured\nresult. The entity association triple extraction, named entity recognition, and\nevent extraction tasks were performed on six datasets NYT11-HRL, DuIE2.0\n, conllpp, MSR , DuEE1.0 [52, 53, 54, 55, 56], and ACE05 in both languages,\ncomparing three metrics of precision, recall, and F1 score.These results sug-","trapped":"/False","creator":"LaTeX with hyperref","source":"./data/Summary of ChatGPTGPT-4 Research.pdf"}


created_at: 2026-06-01 06:01:44
topic: persistent://default/default/d94e1968-cc22-40f0-8810-2412728b970e
id: f8d0c382-b8b3-4fa1-953c-a7edff6b300f
encoding: FLOAT32
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{"page":15,"total_pages":35,"keywords":"","author":"","creationdate":"2023-04-05T00:33:07+00:00","chroma:document":"comparing three metrics of precision, recall, and F1 score.These results sug-\ngest that on six widely used IE datasets, ChatIE improves performance by an\naverage of 18.98% compared to the original ChatGPT without ChatIE, and out-\nperforms the supervised models FCM and MultiR [57, 58] on the NYT11-HRL\ndataset.While the original ChatGPT cannot solve complex IE problems with\noriginal task instructions, and with this framework, successfully IE tasks were\nimplemented on six datasets.Gao et al. [59] explored the feasibility and chal-\nlenges of ChatGPT for event extraction on the ACE2005 corpus, evaluating the\nperformance of ChatGPT in long-tail and complex scenarios (texts containing\nmultiple events) and comparing it with two task-speciﬁc models, Text2Event\nand EEQA [60, 61].Then,they explored the impact of diﬀerent cues on perfor-\nmance of ChatGPT. The results show that the average performance of Chat-\nGPT in long-tail and complex scenarios is only 51.04% of that of task-speciﬁc","moddate":"2023-04-05T00:33:07+00:00","producer":"pdfTeX-1.40.21","page_label":"16","title":"","creator":"LaTeX with hyperref","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","trapped":"/False","subject":""}


created_at: 2026-06-01 06:01:44
topic: persistent://default/default/d94e1968-cc22-40f0-8810-2412728b970e
id: d617ffc2-9015-4a66-8d2d-a6a58d1339e8
encoding: FLOAT32
metadata:
{"moddate":"2023-04-05T00:33:07+00:00","keywords":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","page":15,"producer":"pdfTeX-1.40.21","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","creationdate":"2023-04-05T00:33:07+00:00","total_pages":35,"page_label":"16","author":"","title":"","creator":"LaTeX with hyperref","trapped":"/False","subject":"","chroma:document":"GPT in long-tail and complex scenarios is only 51.04% of that of task-speciﬁc\nmodels such as EEQA. Continuous reﬁnement of cues does not lead to consis-\ntent performance improvements, and ChatGPT is highly sensitive to diﬀerent\ncue styles.Tang et al. [62] proposed a new training paradigm that incorporates\nappropriate cues to guide ChatGPT to generate a variety of examples with dif-\nferent sentence structures and language patterns and eliminate the resulting\nlow-quality or duplicate samples for downstream tasks. Although compared to\na soft model for a speciﬁc healthcare tasks, ChatGPT underperforms in Named\nEntity Recognition (NER) and Relationship Extraction (RE) tasks , in the Gene\nAssociation Database (GAD) Release; EU-ADR corpus for the RE task , the\ninnovative training framework was able to train local models, with F1 scores im-\nproving from 23.37% to 63.99% for the named entity recognition task and from"}


created_at: 2026-06-01 06:01:44
topic: persistent://default/default/d94e1968-cc22-40f0-8810-2412728b970e
id: c17683e0-8d08-47d1-9c5d-2048ef6371c7
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{"title":"","chroma:document":"proving from 23.37% to 63.99% for the named entity recognition task and from\n75%, while alleviating privacy concerns and time-consuming data collection and\nannotation problems.He et al. [63] proposed a contextual learning framework\nICL- D3IE. this framework introduces formatted presentation, continuously it-\nerates to update and improve the presentation, and then combines ChatGPT\nfor text information extraction. In the paper, ICL-D3IE is compared with ex-\nisting pre-trained models such as LiLT,BROS (in-distribution (ID) setting and\nout-of-distribution (OOD) setting) on datasets (FUNSD, CORD, and SROIE\n[64, 65, 66]).These results show that the ICL-D3IE method in all datasets and\nsettings except for the ID setting on CORD are superior to other methods,\n16","author":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","creator":"LaTeX with hyperref","keywords":"","subject":"","page":15,"producer":"pdfTeX-1.40.21","total_pages":35,"creationdate":"2023-04-05T00:33:07+00:00","moddate":"2023-04-05T00:33:07+00:00","trapped":"/False","page_label":"16","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2"}


created_at: 2026-06-01 06:01:44
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{"page":16,"chroma:document":"with ICL-D3IE (GPT-3) F1 scores reaching 90.32% on FUNSD and97.88% on\nSROIE; in the out-of-distribution (OOD) setting, ICL-D3IE performs much\nbetter than previous pre-trained methods on all datasets.Polak et al. [67] pro-\nposed ChatExtract method - consisting of a set of engineering prompts applied\nto a conversational LLM - for automatic data extraction. In the experiment,\nthey extracted a large number of sentences from hundreds of papers and ran-\ndomly selected 100 sentences containing data and 100 sentences without data\nas test data. The results show that the accuracy and recall of LLM exceeded\n90% and may be comparable to human accuracy in many cases; in addition to\nthis, the experiments were conducted under the condition of removing follow-up\nprompts and not keeping the conversation compared to previous experiments,\nrespectively. The accuracy of deleting follow-up questions dropped to 80.2%\nand the recall rate dropped to 88.0%. Removing the conversational aspect and","subject":"","author":"","creator":"LaTeX with hyperref","total_pages":35,"title":"","moddate":"2023-04-05T00:33:07+00:00","keywords":"","producer":"pdfTeX-1.40.21","creationdate":"2023-04-05T00:33:07+00:00","page_label":"17","trapped":"/False","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2"}


created_at: 2026-06-01 06:01:44
topic: persistent://default/default/d94e1968-cc22-40f0-8810-2412728b970e
id: 788d107b-e942-41a0-9290-a1f6308f0a39
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{"trapped":"/False","total_pages":35,"page":16,"moddate":"2023-04-05T00:33:07+00:00","creator":"LaTeX with hyperref","title":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","producer":"pdfTeX-1.40.21","keywords":"","chroma:document":"and the recall rate dropped to 88.0%. Removing the conversational aspect and\nrelated information retention recall and accuracy dropped to 90.0% and 56.6%,\nrespectively, demonstrating the eﬀect of information retention combined with\npurposeful redundancy on LLM information extraction performance.\nQuality Assessment\nFor translation quality, text generation quality, manual assessment is usually\neﬀective but suﬀers from subjectivity and time-consuming, etc. It was found\nthrough exploration that ChatGPT has also achieved signiﬁcant performance\nin automatic quality assessment.\nIn terms of quality assessment,Kocmi et al. [68] proposed a GPT-based\ntranslation quality assessment metric, GEMBA, which evaluates the transla-\ntion of each fragment individually and then averages all the obtained scores to\nobtain a ﬁnal system-level score. In the MQM2022 test set (English-German,\nEnglish-Russian, and Chinese-English) [69], a scoring task was performed with","author":"","creationdate":"2023-04-05T00:33:07+00:00","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","subject":"","page_label":"17"}


created_at: 2026-06-01 06:01:44
topic: persistent://default/default/d94e1968-cc22-40f0-8810-2412728b970e
id: 641553ad-bffa-449a-8724-99a8dda41a39
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{"ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","keywords":"","author":"","total_pages":35,"page":16,"source":"./data/Summary of ChatGPTGPT-4 Research.pdf","page_label":"17","creator":"LaTeX with hyperref","title":"","producer":"pdfTeX-1.40.21","moddate":"2023-04-05T00:33:07+00:00","subject":"","chroma:document":"English-Russian, and Chinese-English) [69], a scoring task was performed with\na classiﬁcation task to compare the accuracy [70] and kendall tau scores [71] of\nseven GPT models under four cue templates.The results showed that GEMBA\nhad the highest system-level accuracy of 88.0% compared to more than 10 au-\ntomatic metrics such as BLEU, and among the seven GPT models, ChatGPT\naccuracy is above 80%, in addition to, the best performance can be obtained in\nthe least constrained template, demonstrating the potential of LLM for trans-\nlation quality assessment tasks, but the evaluation is only applicable at the\nsystem level and needs further improvement.Wang et al. [72] used ChatGPT\nas a natural language generation (NLG) evaluator to study the correlation with\nhuman judgment. On three datasets covering diﬀerent NLG tasks, task- and\naspect-speciﬁc cues were designed to guide ChatGPT for NLG evaluation in\nCNN/DM [73], OpenMEVA-ROC, and BAGEL for summary, story generation,","creationdate":"2023-04-05T00:33:07+00:00","trapped":"/False"}


created_at: 2026-06-01 06:01:44
topic: persistent://default/default/d94e1968-cc22-40f0-8810-2412728b970e
id: 4f450c00-ff83-4ee4-9075-679b80c6eadf
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{"subject":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","moddate":"2023-04-05T00:33:07+00:00","page_label":"17","creator":"LaTeX with hyperref","creationdate":"2023-04-05T00:33:07+00:00","title":"","trapped":"/False","page":16,"source":"./data/Summary of ChatGPTGPT-4 Research.pdf","author":"","keywords":"","chroma:document":"CNN/DM [73], OpenMEVA-ROC, and BAGEL for summary, story generation,\nand data-to-text scoring, respectively. Then,they compute Spearman coeﬃ-\ncients [74],Pearson correlation coeﬃcients [75]. Kendall’s Tau score [76] to as-\nsess the correlation with human evaluations.The results show that ChatGPT is\nhighly correlated with human judgments in all aspects, with correlation coeﬃ-\ncients of 0.4 or more in all categories, showing its potential as an NLG indicator.\n17","total_pages":35,"producer":"pdfTeX-1.40.21"}


created_at: 2026-06-01 06:01:44
topic: persistent://default/default/d94e1968-cc22-40f0-8810-2412728b970e
id: 13f08862-450d-4817-98f1-f02919d157b0
encoding: FLOAT32
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{"creationdate":"2023-04-05T00:33:07+00:00","producer":"pdfTeX-1.40.21","page_label":"18","moddate":"2023-04-05T00:33:07+00:00","trapped":"/False","page":17,"title":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","author":"","chroma:document":"Data Augmentation\nIn natural language processing, text data augmentation is an eﬀective mea-\nsure to alleviate the problem of low data quantity and low quality training data,\nand ChatGPT has shown great potential in this regard.\nIn terms of data augmentation, Dai et al. [77] proposed a ChatGPT-based\ntext data augmentation method that reformulates each sentence in the train-\ning sample into multiple conceptually similar but semantically diﬀerent samples\nfor classiﬁcation tasks downstream of the Bert model.On text transcriptions\nand PubMed 20k datasets containing more than 8 hours of audio data of com-\nmon medical symptom descriptions,experiments were conducted to compare co-\nsine similarity and TransRate metrics with multiple data enhancement methods\n[9].This paper shows that compared with existing data enhancement methods,\nthe proposed ChatAug method shows a double-digit improvement in sentence\nclassiﬁcation accuracy and generates more diverse augmented samples while","creator":"LaTeX with hyperref","subject":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","keywords":"","total_pages":35}


created_at: 2026-06-01 06:01:44
topic: persistent://default/default/d94e1968-cc22-40f0-8810-2412728b970e
id: fa78f6ad-6624-42fa-9f82-f85c260618e7
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{"ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","producer":"pdfTeX-1.40.21","creationdate":"2023-04-05T00:33:07+00:00","page":17,"creator":"LaTeX with hyperref","title":"","subject":"","author":"","keywords":"","chroma:document":"classiﬁcation accuracy and generates more diverse augmented samples while\nmaintaining its accuracy, but the original model is not ﬁne-tuned in the pa-\nper and suﬀers from a lack of domain knowledge, which may produce incorrect\naugmented data.\nMultimodal fusion\nChatGPT can currently only process natural language directly, but with a\ncross-modal encoder, it can combine natural language with cross-modal pro-\ncessing to provide solutions for intelligent transportation, healthcare, and other\nﬁelds.\nIn terms of multimodal data processing, Wu et al. [78] constructed a frame-\nwork that Visual ChatGPT integrates with diﬀerent Visual Foundation Models\n(VFMs) and then combines a series of hints to input visual information to Chat-\nGPT to solve visual problems.The paper shows examples of visual tasks such\nas removing or replacing certain objects from images, interconversion between\nimages and text, demonstrating the Visual ChatGPT has great potential and","page_label":"18","total_pages":35,"source":"./data/Summary of ChatGPTGPT-4 Research.pdf","moddate":"2023-04-05T00:33:07+00:00","trapped":"/False"}


created_at: 2026-06-01 06:01:44
topic: persistent://default/default/d94e1968-cc22-40f0-8810-2412728b970e
id: cbf1f849-4eb6-4683-889f-b9c7aeaea2d3
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{"title":"","author":"","creator":"LaTeX with hyperref","trapped":"/False","creationdate":"2023-04-05T00:33:07+00:00","page":17,"chroma:document":"images and text, demonstrating the Visual ChatGPT has great potential and\ncapability for diﬀerent tasks.But there are issues during the task that require\na large number of hints to convert VFMs to language, invoke multiple VFMs\nto solve complex problems leading to limited real-time capability, and security\nand privacy issues. Zheng et al. [79] showed a text mining example of LLM\nfor extracting self-driving car crash data from California crash news, analyz-\ning a failure report example, and generating a crash report example based on\nkeywords; introduced a use case concept of a smartphone-based framework for\nautomatic LLM failure report generation, which absorbs multiple data sources\ncaptured by cell phone sensors and then transfers the data to a language space\nfor text mining, inference and generation, and further outputs the key informa-\ntion needed to form a comprehensive fault report, demonstrating the potential\nof LLM for a variety of transportation tasks.","producer":"pdfTeX-1.40.21","total_pages":35,"subject":"","moddate":"2023-04-05T00:33:07+00:00","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","keywords":"","page_label":"18","source":"./data/Summary of ChatGPTGPT-4 Research.pdf"}


created_at: 2026-06-01 06:01:44
topic: persistent://default/default/d94e1968-cc22-40f0-8810-2412728b970e
id: c2d2097a-b12f-4b48-92fd-6536587996de
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{"total_pages":35,"chroma:document":"of LLM for a variety of transportation tasks.\nNowadays, ChatGPT shows a wide range of applications in data visualiza-\ntion, information extraction, data enhancement, quality assessment, and multi-\nmodal data processing.But there are also issues on how to further utilize hints\nto eﬀectively interact with ChatGPT, lack of ability to process and analyze data\n18","title":"","author":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","creationdate":"2023-04-05T00:33:07+00:00","subject":"","keywords":"","trapped":"/False","creator":"LaTeX with hyperref","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","page":17,"page_label":"18","producer":"pdfTeX-1.40.21","moddate":"2023-04-05T00:33:07+00:00"}


created_at: 2026-06-01 06:01:44
topic: persistent://default/default/d94e1968-cc22-40f0-8810-2412728b970e
id: 47c9b937-2ad6-4fb5-9e8d-e5c998ffbc48
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{"ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","page":18,"source":"./data/Summary of ChatGPTGPT-4 Research.pdf","author":"","title":"","creator":"LaTeX with hyperref","creationdate":"2023-04-05T00:33:07+00:00","keywords":"","page_label":"19","trapped":"/False","moddate":"2023-04-05T00:33:07+00:00","total_pages":35,"producer":"pdfTeX-1.40.21","subject":"","chroma:document":"from devices such as sensors, and data privacy and security.\nCueing Techniques\nCue engineering provides important support for eﬀective dialogue with large\nlanguage models.White et al. [80] proposed a framework for cueing models\napplicable to diﬀerent domains. This framework structures cues to interact\nwith LLMs by providing speciﬁc rules and guidelines. Also, this paper presents\na catalog of cueing patterns that have been applied to LLM interactions, as well\nas speciﬁc examples with and without cues. The advantages of the combinability\nof prompting patterns are demonstrated, allowing users to interact with LLM\nmore eﬀectively, but patterns for reusable solutions and new ways to use LLM\nneed to be continuously explored.\n2.1.7 Human-ChatGPT Collaboration\nCollaboration between humans and machines is a process where humans and\nmachines work together to achieve a common goal. In such collaboration, hu-\nmans provide domain expertise, creativity, and decision-making abilities, while"}


created_at: 2026-06-01 06:01:44
topic: persistent://default/default/d94e1968-cc22-40f0-8810-2412728b970e
id: d9075dc0-a622-48cf-8100-db7722dfd1b9
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{"total_pages":35,"keywords":"","moddate":"2023-04-05T00:33:07+00:00","chroma:document":"mans provide domain expertise, creativity, and decision-making abilities, while\nmachines provide automation, scalability, and computing power. ChatGPT is\nan advanced natural language processing model that can understand and gen-\nerate human-like language, thereby reducing communication costs. Its ability\nto process and generate natural language makes it an ideal partner for human\ncollaboration. ChatGPT can oﬀer relevant suggestions, complete tasks based\non human input, and enhance human productivity and creativity. It can learn\nfrom human feedback and adapt to new tasks and domains, further improv-\ning its performance in human-machine collaboration. ChatGPT’s capability to\ncomprehend natural language and produce appropriate responses makes it a\nvaluable tool for various collaboration applications, as demonstrated by several\nstudies in the literature we have gathered.\nAhmad et al. [81] proposed a method for human-machine collaboration us-","subject":"","creationdate":"2023-04-05T00:33:07+00:00","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","page":18,"page_label":"19","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","producer":"pdfTeX-1.40.21","trapped":"/False","title":"","creator":"LaTeX with hyperref","author":""}


created_at: 2026-06-01 06:01:44
topic: persistent://default/default/d94e1968-cc22-40f0-8810-2412728b970e
id: b3dcb298-5652-41fe-80e0-8a1bf9e95591
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{"author":"","trapped":"/False","keywords":"","title":"","total_pages":35,"moddate":"2023-04-05T00:33:07+00:00","chroma:document":"Ahmad et al. [81] proposed a method for human-machine collaboration us-\ning ChatGPT to create software architecture. This method transforms software\nstories (created by software architects based on application scenarios) into feasi-\nble software architecture diagrams through continuous interaction between the\nsoftware architect and ChatGPT. During the evaluation stage, ChatGPT uses\nthe Software Architecture Analysis Method (SAAM) to evaluate each compo-\nnent in the software architecture and generate evaluation reports. This method\neﬃciently utilizes the knowledge and supervision of the architect with the ca-\npabilities of ChatGPT to collaboratively build software-intensive systems and\nservices. Lanzi et al. [82] proposed a collaborative design framework that com-\nbines interactive evolution and ChatGPT to simulate typical human design pro-\ncesses. Humans collaborate with large language models (such as ChatGPT) to","page":18,"source":"./data/Summary of ChatGPTGPT-4 Research.pdf","creationdate":"2023-04-05T00:33:07+00:00","producer":"pdfTeX-1.40.21","page_label":"19","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","subject":"","creator":"LaTeX with hyperref"}


created_at: 2026-06-01 06:01:44
topic: persistent://default/default/d94e1968-cc22-40f0-8810-2412728b970e
id: 157cce3b-b13f-4a2c-8cb2-e60d6473e4c6
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{"author":"","creationdate":"2023-04-05T00:33:07+00:00","title":"","page":18,"trapped":"/False","creator":"LaTeX with hyperref","keywords":"","page_label":"19","chroma:document":"cesses. Humans collaborate with large language models (such as ChatGPT) to\nrecombine and transform ideas, and use genetic algorithms to iterate through\ncomplex creative tasks. The results of three game design tasks showed that the\nframework received positive feedback from game designers. The framework has\ngood reusability and can be applied to any design task that can be described in\nfree text form.\n19","producer":"pdfTeX-1.40.21","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","moddate":"2023-04-05T00:33:07+00:00","subject":"","total_pages":35}


created_at: 2026-06-01 06:01:44
topic: persistent://default/default/d94e1968-cc22-40f0-8810-2412728b970e
id: b5c014f4-1ec5-499c-8f14-10f6b9367743
encoding: FLOAT32
metadata:
{"keywords":"","title":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","subject":"","author":"","trapped":"/False","total_pages":35,"creationdate":"2023-04-05T00:33:07+00:00","page_label":"20","moddate":"2023-04-05T00:33:07+00:00","chroma:document":"In the future, ChatGPT’s ability to understand nonverbal cues such as tone\nof voice and body language can be enhanced, enabling it to better understand\nhuman thoughts and interact with people more eﬀectively.\n2.1.8 ChatGPT Integration\nIntegration refers to combining diﬀerent systems or software components to\nachieve a common goal. ChatGPT can be integrated as a part of a whole or\nact as an integration tool to enable seamless communication between diﬀerent\nsystems. Its natural language processing ability makes it easier for non-technical\nusers to interact with systems, reducing the need for specialized knowledge or\ntraining. Some studies in the literature we collected have already demonstrated\nthis.\nTreude et al. [39] integrated ChatGPT into the prototype of ”GPTCOM-\nCARE” to address programming query problems. This integration allowed for\nthe generation of multiple source code solutions for the same query, which in-","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","creator":"LaTeX with hyperref","page":19,"producer":"pdfTeX-1.40.21"}


created_at: 2026-06-01 06:01:44
topic: persistent://default/default/d94e1968-cc22-40f0-8810-2412728b970e
id: b5dff00c-b467-4d31-8dad-e2a50fe7056f
encoding: FLOAT32
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{"author":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","chroma:document":"the generation of multiple source code solutions for the same query, which in-\ncreased the eﬃciency of software development. The results of their study demon-\nstrated the eﬀectiveness of using ChatGPT to improve the quality and diversity\nof code solutions, ultimately reducing the amount of time and eﬀort required for\nsoftware development.Wang et al. [83] proposed the chatCAD method, which\nutilizes large language models (LLMs) such as ChatGPT to enhance the out-\nput of multiple CAD networks for medical images, including diagnosis, lesion\nsegmentation, and report generation networks. The method generates sugges-\ntions in the form of a chat dialogue. The authors tested the eﬀectiveness of\nthe method on a randomly selected set of 300 cases from the MIMIC-CXR\ndataset, which included 50 cases each of cardiomegaly, edema, consolidation,\natelectasis, pleural eﬀusion, and no ﬁndings. Compared to CvT2DistilGPT2\nand R2GenCMN, chatCAD showed signiﬁcant advantages in RC and F1, while","page_label":"20","creator":"LaTeX with hyperref","trapped":"/False","subject":"","creationdate":"2023-04-05T00:33:07+00:00","producer":"pdfTeX-1.40.21","title":"","page":19,"keywords":"","total_pages":35,"moddate":"2023-04-05T00:33:07+00:00"}


created_at: 2026-06-01 06:01:44
topic: persistent://default/default/d94e1968-cc22-40f0-8810-2412728b970e
id: fda188ab-2c48-4c3b-a1b4-9a74b466ef7e
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{"chroma:document":"and R2GenCMN, chatCAD showed signiﬁcant advantages in RC and F1, while\nonly performing weaker than R2GenCMN in PR.\nIntegrating ChatGPT into applications will still present challenges. Firstly,\nChatGPT’s performance may be aﬀected by language barriers or diﬀerences\nin terminology between diﬀerent systems. Additionally, ChatGPT’s responses\nare not always deterministic, which poses a challenge when integrating with\nsystems that require precise and reproducible results. Finally, the processing\ntime of ChatGPT is slow for integration tasks involving time-sensitive data such\nas traﬃc, which is a limitation in time-critical environments.\n2.2 AI Ethics\nSince the advent of ChatGPT, this powerful natural language processing model\nhas not only brought great convenience to people but also triggered more crisis-\naware thinking. Some researchers have started to hypothesize and study the\npotential negative impacts of ChatGPT. This proactive research provides good","creator":"LaTeX with hyperref","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","author":"","page":19,"subject":"","trapped":"/False","moddate":"2023-04-05T00:33:07+00:00","total_pages":35,"title":"","creationdate":"2023-04-05T00:33:07+00:00","keywords":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","page_label":"20","producer":"pdfTeX-1.40.21"}


created_at: 2026-06-01 06:01:44
topic: persistent://default/default/d94e1968-cc22-40f0-8810-2412728b970e
id: fc06a3b4-d582-4273-aaf3-d2f5bd0e87e5
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{"subject":"","title":"","producer":"pdfTeX-1.40.21","creationdate":"2023-04-05T00:33:07+00:00","creator":"LaTeX with hyperref","total_pages":35,"page":19,"chroma:document":"potential negative impacts of ChatGPT. This proactive research provides good\nproposals for standardized construction to address future AI abuse issues.\nRegarding the evaluation of ChatGPT’s own political and ethical tendencies,\nHartmann et al. [84] used Wahl-O-Mat, one of the most commonly used voting\n20","author":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","page_label":"20","trapped":"/False","moddate":"2023-04-05T00:33:07+00:00","keywords":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2"}


created_at: 2026-06-01 06:01:44
topic: persistent://default/default/d94e1968-cc22-40f0-8810-2412728b970e
id: 9e7ac074-2c69-4162-95d1-5691fc151001
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{"chroma:document":"advice applications in the world, to show ChatGPT political statements from\ndiﬀerent parties, forcing it to make choices of agree, disagree, or neutral. The\nresults indicated that ChatGPT has a pro-environment, left-wing liberal ide-\nology, which was also conﬁrmed in the nation-state agnostic political compass\ntest. Another study (referenced as [85]) examined ChatGPT’s moral standards\nby repeatedly asking it diﬀerent versions of the trolley problem, and found that\nChatGPT gave answers with diﬀerent moral orientations, lacking a ﬁrm moral\nstance. A subsequent test also found that ChatGPT’s lack of consistency could\naﬀect people’s moral judgments. Additionally, Borji et al. [86] demonstrated\nChatGPT’s inconsistency in reasoning, factual errors, mathematics, coding, and\nbias across eleven related aspects. These ﬁndings highlight ChatGPT’s inher-\nent traits and limitations, and people should be aware of their potential impact\nwhen seeking advice from ChatGPT.","title":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","author":"","page":20,"total_pages":35,"keywords":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","trapped":"/False","creationdate":"2023-04-05T00:33:07+00:00","subject":"","page_label":"21","creator":"LaTeX with hyperref","producer":"pdfTeX-1.40.21","moddate":"2023-04-05T00:33:07+00:00"}


created_at: 2026-06-01 06:01:44
topic: persistent://default/default/d94e1968-cc22-40f0-8810-2412728b970e
id: 9a9a62bb-8692-4be6-8250-a8b6e3c3f05b
encoding: FLOAT32
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{"author":"","page_label":"21","creator":"LaTeX with hyperref","trapped":"/False","keywords":"","title":"","moddate":"2023-04-05T00:33:07+00:00","total_pages":35,"chroma:document":"when seeking advice from ChatGPT.\nRegarding relevant policies and regulations, Hacker et al. [87] discussed the\nnature and rules of large generative AI models, including ChatGPT, which are\nrapidly changing the way we communicate, explain, and create. The author\nsuggested that diﬀerent stakeholders in the value chain should take regulatory\nresponsibility and deploy four strategies to tailor more comprehensive laws for\nthe beneﬁt of society. Another study (referenced as [88]) criticized the European\nCommission’s proposal on AI responsibility and suggested revising the proposed\nAI responsibility framework to ensure eﬀective compensation while promoting\ninnovation, legal certainty, and sustainable AI regulation. A policy framework\nwas proposed (referenced as [89]) to customize LLMs, such as ChatGPT, in a so-\ncially acceptable and safe manner, emphasizing the need to align large language\nmodels (LLMs) with human preferences.","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","producer":"pdfTeX-1.40.21","subject":"","page":20,"creationdate":"2023-04-05T00:33:07+00:00"}


created_at: 2026-06-01 06:01:44
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id: 97b5b0b4-f862-465f-8dbe-e5fc6fe10120
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{"source":"./data/Summary of ChatGPTGPT-4 Research.pdf","keywords":"","title":"","author":"","page_label":"21","page":20,"total_pages":35,"ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","moddate":"2023-04-05T00:33:07+00:00","trapped":"/False","creationdate":"2023-04-05T00:33:07+00:00","subject":"","creator":"LaTeX with hyperref","producer":"pdfTeX-1.40.21","chroma:document":"models (LLMs) with human preferences.\nThe political and ethical tendencies of ChatGPT could inﬂuence users’ be-\nhavior and decision-making to some extent. However, some studies have con-\nducted in-depth research on the use of norms and limitations, which could enable\nhumans to use ChatGPT more reasonably and safely.\n2.3 Evaluation\n2.3.1 Comparison of ChatGPT with existing popular models\nWe use publicly available datasets to comprehensively evaluate the strengths\nand limitations of ChatGPT. Reference [90] evaluates the technical performance\nof ChatGPT in multitask, multilingual, and multimodal aspects based on 23\nstandard public datasets and newly designed multimodal datasets, including\neight diﬀerent common natural language processing application tasks. The ex-\nperimental results show that, in terms of multitasking, ChatGPT outperforms\nvarious state-of-the-art zero-shot learning large language models in most tasks,"}


created_at: 2026-06-01 06:01:44
topic: persistent://default/default/d94e1968-cc22-40f0-8810-2412728b970e
id: fdd4c1a9-f47f-4242-93a3-b9abf190f9af
encoding: FLOAT32
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{"creator":"LaTeX with hyperref","title":"","moddate":"2023-04-05T00:33:07+00:00","producer":"pdfTeX-1.40.21","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","page_label":"21","author":"","subject":"","keywords":"","creationdate":"2023-04-05T00:33:07+00:00","trapped":"/False","page":20,"chroma:document":"various state-of-the-art zero-shot learning large language models in most tasks,\nand even outperforms ﬁne-tuned task-speciﬁc models in some individual tasks.\nIn terms of multilingualism, we found that ChatGPT cannot be applied to low-\nresource languages because it cannot understand the language and generate\ntranslations for that language. In terms of multimodality, ChatGPT’s ability is\nstill basic compared to specialized language-visual models.\n21","total_pages":35}


created_at: 2026-06-01 06:01:44
topic: persistent://default/default/d94e1968-cc22-40f0-8810-2412728b970e
id: 2b10f66f-e550-46df-9e6c-6d5ba71bbeda
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{"author":"","page":21,"creationdate":"2023-04-05T00:33:07+00:00","moddate":"2023-04-05T00:33:07+00:00","chroma:document":"In terms of stability, reference [91] concludes that ChatGPT’s performance is\nalways lower than SOTA, the current state-of-the-art model, in almost all tasks.\nThis means that as a general model, ChatGPT has never reached the level of the\nbest existing models. Experimental data shows that the average quality of the\nSOTA model is 73.7%, while the average quality of the ChatGPT model is only\n56.5%. At the same time, ChatGPT’s stability is poor: the standard deviation\nof its performance is 23.3%, while the SOTA model’s standard deviation is\nonly 16.7%. This non-deterministic behavior exhibited by ChatGPT could be\na serious drawback in some problems.\nSimilarly, Qin et al. [92] conducted a comprehensive evaluation of whether\nChatGPT is a qualiﬁed general natural language processing task solver. The ex-\nperiment analyzed ChatGPT’s zero-shot learning ability based on 20 commonly\nused public datasets covering 7 representative task categories. Below, we will","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","total_pages":35,"trapped":"/False","keywords":"","subject":"","page_label":"22","title":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","producer":"pdfTeX-1.40.21","creator":"LaTeX with hyperref"}


created_at: 2026-06-01 06:01:44
topic: persistent://default/default/d94e1968-cc22-40f0-8810-2412728b970e
id: e3935177-eb0b-4e02-b847-3dc2f53cf3e3
encoding: FLOAT32
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{"total_pages":35,"chroma:document":"used public datasets covering 7 representative task categories. Below, we will\nanalyze ChatGPT’s performance on each task:\nIn terms of reasoning tasks, ChatGPT performs average on mathematical\nsymbol, commonsense causal, and logical reasoning tasks, but performs well\nin arithmetic reasoning [92]. That is to say, ChatGPT’s abilities vary among\ndiﬀerent types of reasoning tasks. In terms of logical reasoning, ChatGPT’s\ndeductive and abductive reasoning are superior to inductive reasoning, while\nin other reasoning tasks, such as analogy, causal and commonsense reasoning,\nChatGPT performs well [90].\nIn terms of sentiment analysis task, ChatGPT performs similarly to GPT-3.5\nand bert-style models [92, 93]. However, according to literature [91], ChatGPT\nhas losses not exceeding 25% on most tasks, except for three relatively sub-\njective emotion perception tasks where it performs poorly. If we remove these\ntasks to calculate the average quality of the two models, we ﬁnd that the SOTA","keywords":"","trapped":"/False","creator":"LaTeX with hyperref","producer":"pdfTeX-1.40.21","page_label":"22","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","subject":"","moddate":"2023-04-05T00:33:07+00:00","title":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","creationdate":"2023-04-05T00:33:07+00:00","author":"","page":21}


created_at: 2026-06-01 06:01:44
topic: persistent://default/default/d94e1968-cc22-40f0-8810-2412728b970e
id: 2472d178-dd57-489d-a3fb-8e66234e5344
encoding: FLOAT32
metadata:
{"source":"./data/Summary of ChatGPTGPT-4 Research.pdf","creationdate":"2023-04-05T00:33:07+00:00","page":21,"ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","author":"","title":"","chroma:document":"tasks to calculate the average quality of the two models, we ﬁnd that the SOTA\nmethod has an average quality of 80%, while the ChatGPT method has an av-\nerage quality of 69.7%. That is to say, ChatGPT performs well on all tasks\nexcept for emotion-related tasks, and can handle most of the problems we con-\nsider. However, overall, its performance is lower than the SOTA model based\non experimental data, but the diﬀerence between the two is not very large.\nIn other tasks, according to literature [92], ChatGPT performs well in nat-\nural language inference, i.e., the task of inferring sentence relationships, and its\nperformance on this task is signiﬁcantly better than all bert-style models [93].\nHowever, while ChatGPT performs well on inference tasks, it may produce some\nself-contradictory or unreasonable responses, which is its potential limitation.\nIn question-answering, dialogue, and summarization tasks, ChatGPT performs","moddate":"2023-04-05T00:33:07+00:00","keywords":"","trapped":"/False","subject":"","producer":"pdfTeX-1.40.21","page_label":"22","creator":"LaTeX with hyperref","total_pages":35}


created_at: 2026-06-01 06:01:44
topic: persistent://default/default/d94e1968-cc22-40f0-8810-2412728b970e
id: 2e1159fd-ab25-4227-9d0d-b85d06708bd7
encoding: FLOAT32
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{"creator":"LaTeX with hyperref","chroma:document":"In question-answering, dialogue, and summarization tasks, ChatGPT performs\nbetter than the GPT-3.5 model [92], especially in the question-answering task,\nwhere its performance is comparable to bert-style models [93]. Therefore, we\nhave demonstrated that ChatGPT is a qualiﬁed general-purpose model.\nHowever, ChatGPT also has limitations in many aspects. Firstly, it lacks the\nability to handle non-textual semantic reasoning tasks such as mathematical,\ntemporal, and spatial reasoning, and it performs poorly in multi-hop reasoning\n[90]. Secondly, ChatGPT is not good at solving named entity recognition tasks\n[92]. Furthermore, ChatGPT performs poorly in handling tasks involving nega-\ntive connotations and neutral similarity [93]. Finally, these conclusions indicate\n22","page_label":"22","page":21,"author":"","subject":"","trapped":"/False","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","moddate":"2023-04-05T00:33:07+00:00","creationdate":"2023-04-05T00:33:07+00:00","producer":"pdfTeX-1.40.21","keywords":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","total_pages":35,"title":""}


created_at: 2026-06-01 06:01:44
topic: persistent://default/default/d94e1968-cc22-40f0-8810-2412728b970e
id: ea2e0211-8418-40dc-98ea-ffb6d953608f
encoding: FLOAT32
metadata:
{"moddate":"2023-04-05T00:33:07+00:00","subject":"","page_label":"23","keywords":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","creator":"LaTeX with hyperref","page":22,"producer":"pdfTeX-1.40.21","total_pages":35,"trapped":"/False","author":"","creationdate":"2023-04-05T00:33:07+00:00","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","chroma:document":"that, like other large pre-trained language models, ChatGPT has limitations in\ncompleting complex reasoning tasks.\nIn summary, ChatGPT’s zero-shot performance is comparable to ﬁne-tuned\nbert and GPT-3.5 models, and with the help of advanced prompting strategies,\nChatGPT can demonstrate better comprehension abilities. However, it still\ncannot outperform the current SOTA models.\n2.3.2 The possibility of using ChatGPT for plagiarism and cheating\nIn response to the possibility of ChatGPT being used for plagiarism and cheat-\ning, Zhou et al. [94] reﬂected on the current state of development of artiﬁcial in-\ntelligence like ChatGPT. As ChatGPT becomes increasingly easy to obtain and\nscalable in text generation, there is a high likelihood that these technologies will\nbe used for plagiarism, including scientiﬁc literature and news sources, posing\na great threat to the credibility of various forms of news media and academic","title":""}


created_at: 2026-06-01 06:01:44
topic: persistent://default/default/d94e1968-cc22-40f0-8810-2412728b970e
id: b51e5932-d487-478f-8725-d2bca09d522c
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{"producer":"pdfTeX-1.40.21","chroma:document":"a great threat to the credibility of various forms of news media and academic\narticles. Some scholars are concerned that the end of paper as a meaningful\nevaluation tool may be approaching [95, 96], as ChatGPT can easily generate\npersuasive paragraphs, chapters, and papers on any given topic. Additionally,\nit will exacerbate plagiarism issues in many ﬁelds such as education, medicine,\nand law [10], and may be used for cheating in academic exams [97]. Deﬁnitional\nrecognition technology is a relatively eﬀective method for detecting plagiarism,\nand the deﬁnitional typology proposed in [94] can alleviate people’s concerns by\nbeing used to construct new datasets. Susnjak [97] proposed a solution to the\npossibility of large language models like ChatGPT being used for exam cheat-\ning: guiding ChatGPT to generate some critical thinking problems through\nquestioning, then providing answers and critically evaluating them. Analysis of","creator":"LaTeX with hyperref","title":"","subject":"","trapped":"/False","total_pages":35,"keywords":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","author":"","moddate":"2023-04-05T00:33:07+00:00","page":22,"page_label":"23","creationdate":"2023-04-05T00:33:07+00:00"}


created_at: 2026-06-01 06:01:44
topic: persistent://default/default/d94e1968-cc22-40f0-8810-2412728b970e
id: 07798656-ed72-455d-81b1-aca7d5f6f59d
encoding: FLOAT32
metadata:
{"page":22,"creationdate":"2023-04-05T00:33:07+00:00","moddate":"2023-04-05T00:33:07+00:00","creator":"LaTeX with hyperref","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","chroma:document":"questioning, then providing answers and critically evaluating them. Analysis of\nChatGPT shows that it exhibits critical thinking, can generate highly realistic\ntext in terms of accuracy, relevance, depth, breadth, logic, persuasiveness, and\noriginality. Therefore, educators must be aware of the possibility of ChatGPT\nbeing used for exam cheating and take measures to combat cheating behavior\nto ensure the fairness of online exams.\n2.3.3 Feedback from ChatGPT users\nIn response to feedback from ChatGPT users, Haque et al. [98] conducted a\nmixed-methods study using 10,732 early ChatGPT user tweets. The authors\nextracted Twitter data using Python and Twitter API and constructed the\nChatGPTTweet dataset, which contains 18k tweets. For each tweet, the au-\nthors collected information on text content, user location, occupation, veriﬁca-\ntion status, date of publication, and tags. Based on this dataset, the authors\nstudied the characteristics of early ChatGPT users, discussion topics related to","keywords":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","page_label":"23","producer":"pdfTeX-1.40.21","author":"","trapped":"/False","total_pages":35,"subject":"","title":""}


created_at: 2026-06-01 06:01:44
topic: persistent://default/default/d94e1968-cc22-40f0-8810-2412728b970e
id: b43baeef-66b9-4cba-ac6f-0869f41e813f
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{"keywords":"","subject":"","page":22,"creator":"LaTeX with hyperref","moddate":"2023-04-05T00:33:07+00:00","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","author":"","total_pages":35,"ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","title":"","trapped":"/False","creationdate":"2023-04-05T00:33:07+00:00","chroma:document":"studied the characteristics of early ChatGPT users, discussion topics related to\nChatGPT on Twitter, and the sentiment of Twitter users toward ChatGPT.\nFor RQ1, the authors found that early ChatGPT users had a diverse and wide\nrange of occupational backgrounds and geographical locations. For RQ2, the\nauthors identiﬁed nine topics related to ChatGPT, including its impact on soft-\nware development, entertainment and creativity, natural language processing,\n23","page_label":"23","producer":"pdfTeX-1.40.21"}


created_at: 2026-06-01 06:01:44
topic: persistent://default/default/d94e1968-cc22-40f0-8810-2412728b970e
id: 0990e015-86d9-41cc-893a-5956c6377c60
encoding: FLOAT32
metadata:
{"moddate":"2023-04-05T00:33:07+00:00","keywords":"","page":23,"creator":"LaTeX with hyperref","total_pages":35,"creationdate":"2023-04-05T00:33:07+00:00","subject":"","title":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","trapped":"/False","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","page_label":"24","author":"","producer":"pdfTeX-1.40.21","chroma:document":"education, chatbot intelligence, business development, search engines, question-\nanswering tests, and future careers and opportunities. For RQ3, most early\nusers expressed positive sentiment toward topics such as software development\nand creativity, while only a few expressed concern about the potential misuse\nof ChatGPT.\n2.3.4 Adverse eﬀects of ChatGPT on users\nRegarding the negative eﬀects of ChatGPT on users, Luan et al. [99] studied\nthe psychological principles of ChatGPT, delved into the factors that attract\nusers’ attention, and revealed the impact of these factors on future learning.\nIn the post-pandemic era, teachers and students are both facing uncertainty\nin the teaching process and job pressures. Under these common constraints of\neducation and employment, educators and students must re-evaluate current\neducational methods and outcomes, as well as students’ future career devel-\nopment. Through question-and-answer exchanges with ChatGPT, people can"}


created_at: 2026-06-01 06:01:44
topic: persistent://default/default/d94e1968-cc22-40f0-8810-2412728b970e
id: 02edb3ba-83e6-4d60-9607-18aad9f1f0fd
encoding: FLOAT32
metadata:
{"chroma:document":"opment. Through question-and-answer exchanges with ChatGPT, people can\neasily obtain appropriate solutions or key information, thereby enhancing their\nmotivation, eliminating anxiety in learning, improving interest, and achieving\npsychological satisfaction. Subhash et al. [100] explored whether large language\nmodels have the ability to reverse user preferences. With the development of\npre-trained large language models, people are increasingly concerned about the\nability of these models to inﬂuence, persuade, and potentially manipulate user\npreferences in extreme cases. Therefore, the literature [100] roughly qualita-\ntively analyzed that adversarial behavior does lead to potential changes in user\npreferences and behaviors in dialogue systems. If we want to further quanti-\ntatively analyze the ability of large language models in this regard, additional\nstatistical summary techniques need to be used for future research.\n3 Discussion\n3.1 Limitations","trapped":"/False","page":23,"creationdate":"2023-04-05T00:33:07+00:00","subject":"","creator":"LaTeX with hyperref","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","moddate":"2023-04-05T00:33:07+00:00","total_pages":35,"title":"","keywords":"","author":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","producer":"pdfTeX-1.40.21","page_label":"24"}


created_at: 2026-06-01 06:01:44
topic: persistent://default/default/d94e1968-cc22-40f0-8810-2412728b970e
id: 3af46a68-d3f9-4554-afc3-eed2e768ee83
encoding: FLOAT32
metadata:
{"total_pages":35,"page_label":"24","trapped":"/False","keywords":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","creator":"LaTeX with hyperref","author":"","title":"","producer":"pdfTeX-1.40.21","moddate":"2023-04-05T00:33:07+00:00","subject":"","page":23,"chroma:document":"statistical summary techniques need to be used for future research.\n3 Discussion\n3.1 Limitations\nDespite the remarkable capabilities of ChatGPT and GPT-4, it still faces certain\nlimitations. Some of these limitations include:\nOutdated Knowledge\nThe current models are trained on historical data (up to 2021), thereby\nlacking real-time comprehension of current aﬀairs. This is a critical concern\nin today’s information-explosion era, as the reliability of prior knowledge bases\nprogressively diminishes, potentially yielding inaccurate responses, especially in\nrapidly evolving domains such as jurisprudence and technology. Additionally,\nthese models are incapable of fact-checking while the training data is composed\nof content from various sources, some of which may be unreliable, which may\nresult in seemingly plausible yet nonsensical responses.\n24","creationdate":"2023-04-05T00:33:07+00:00"}


created_at: 2026-06-01 06:01:44
topic: persistent://default/default/d94e1968-cc22-40f0-8810-2412728b970e
id: 513e0854-ad23-4200-b697-0382851761ed
encoding: FLOAT32
metadata:
{"creationdate":"2023-04-05T00:33:07+00:00","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","producer":"pdfTeX-1.40.21","creator":"LaTeX with hyperref","moddate":"2023-04-05T00:33:07+00:00","title":"","page":24,"subject":"","keywords":"","chroma:document":"Insuﬃcient Understanding\nWhile these models can interpret the majority of inquiries and contextual\nsituations, they occasionally encounter comprehension biases when addressing\nambiguous or contextually complex queries. Furthermore, in certain specialized\nﬁelds, the abundance of unique abbreviation exacerbates the models’ under-\nstanding challenges, resulting in incorrect and vacuous responses.\nEnergy Consumption\nThroughout the training and inference stages, these large-scale models re-\nquire signiﬁcant computational resources and electrical power, resulting in ele-\nvated energy consumption and signiﬁcant carbon emissions. Consequently, this\nrestricts their deployment and practical applications.\nMalicious Usage\nDespite OpenAI implementing a series of restrictions to mitigate model tox-\nicity, instances of users evading these constraints through meticulously designed\nprompts have emerged, inducing the model to produce unhealthy content or\neven using it for illicit commercial purposes.","total_pages":35,"author":"","page_label":"25","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","trapped":"/False"}


created_at: 2026-06-01 06:01:44
topic: persistent://default/default/d94e1968-cc22-40f0-8810-2412728b970e
id: 8c84aba4-e422-428b-a9c1-3d94b7fa9edc
encoding: FLOAT32
metadata:
{"moddate":"2023-04-05T00:33:07+00:00","creationdate":"2023-04-05T00:33:07+00:00","page":24,"chroma:document":"even using it for illicit commercial purposes.\nBias and Discrimination\nDue to the inﬂuence of pre-training data, the models exhibit biases in polit-\nical, ideological, and other areas. The application of LLMs in public domains,\nsuch as education and publicity, should be approached with extreme caution.\nPrivacy and Data Security\nConcurrent with the expansion of users, protecting user privacy and data\nsecurity becomes increasingly important. In fact, ChatGPT was banned in\nItaly in early April due to privacy concerns. This is particularly relevant given\nthe models’ extensive collection of personal information and preferences during\ninteractions, and as future multimodal models, such as GPT-4, may frequently\nrequire users to upload private photos.\n3.2 Future Directions\nIn forthcoming research, the development of models based on ChatGPT and\nGPT-4 may focus on addressing these limitations to enhance their practical\napplications.","page_label":"25","total_pages":35,"keywords":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","creator":"LaTeX with hyperref","trapped":"/False","producer":"pdfTeX-1.40.21","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","subject":"","title":"","author":""}


created_at: 2026-06-01 06:01:44
topic: persistent://default/default/d94e1968-cc22-40f0-8810-2412728b970e
id: b60b466c-5765-42b8-918a-1b5f51aa5b6c
encoding: FLOAT32
metadata:
{"keywords":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","producer":"pdfTeX-1.40.21","chroma:document":"GPT-4 may focus on addressing these limitations to enhance their practical\napplications.\nPrimarily, researchers should continue to work on reﬁning model training\nmethodologies while ﬁltering pre-training data to minimize the presence of mis-\nleading information in the model’s knowledge base, thereby obtaining accurate\nresponses. Concurrently, it is crucial to emphasize training approaches that\neconomize computational resources, thereby mitigating costs and broadening\npotential application scenarios.\nMoreover, the advancements in context-awareness and disambiguation tech-\nnologies are anticipated to facilitate enhanced comprehension of complex queries\n25","title":"","trapped":"/False","author":"","moddate":"2023-04-05T00:33:07+00:00","creationdate":"2023-04-05T00:33:07+00:00","total_pages":35,"source":"./data/Summary of ChatGPTGPT-4 Research.pdf","creator":"LaTeX with hyperref","page":24,"subject":"","page_label":"25"}


created_at: 2026-06-01 06:01:44
topic: persistent://default/default/d94e1968-cc22-40f0-8810-2412728b970e
id: 907bcdeb-72ec-4b79-8d39-df03c419cea0
encoding: FLOAT32
metadata:
{"subject":"","moddate":"2023-04-05T00:33:07+00:00","page":25,"author":"","trapped":"/False","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","producer":"pdfTeX-1.40.21","page_label":"26","chroma:document":"by models, improving the accuracy, relevance, and context-awareness of AI-\ngenerated content. Integrating real-time data streams can also keep these mod-\nels in sync with current events and trends, enabling them to provide up-to-date\ninformation such as live traﬃc, weather, and stock updates.\nAdditionally, developers should engage in interdisciplinary collaboration with\nspecialists from diverse domains, including policy-making, jurisprudence, and\nsociology, with the objective of formulating standard and ethical frameworks\nfor LLM development, deployment, and utilization, thereby alleviating poten-\ntial harmful consequences. In terms of public awareness and education, manda-\ntory awareness training should be implemented prior to large-scale public de-\nployment and application to increase public awareness of LLM capabilities and\nlimitations while promoting responsible and informed utilization, especially in\nindustries such as K-12 education and journalism.","creator":"LaTeX with hyperref","creationdate":"2023-04-05T00:33:07+00:00","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","total_pages":35,"title":"","keywords":""}


created_at: 2026-06-01 06:01:44
topic: persistent://default/default/d94e1968-cc22-40f0-8810-2412728b970e
id: 6cddad5f-7203-4957-86fd-e2cb01ec2818
encoding: FLOAT32
metadata:
{"creationdate":"2023-04-05T00:33:07+00:00","keywords":"","producer":"pdfTeX-1.40.21","page":25,"moddate":"2023-04-05T00:33:07+00:00","page_label":"26","creator":"LaTeX with hyperref","author":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","title":"","total_pages":35,"ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","subject":"","trapped":"/False","chroma:document":"industries such as K-12 education and journalism.\nFinally, the inﬂuence of ChatGPT and GPT-4 should not be limited to\njust the NLP ﬁeld. They also show promising prospects in the areas of com-\nputer vision, brain-inspired AI, and robotics. These models exhibit a capacity\nfor learning and comprehension comparable with human-level intelligence, po-\nsitioning them as a pivotal component in the development of artiﬁcial general\nintelligence (AGI)[101]. Their ability to facilitate seamless interactions between\nhumans and robots paves the way for the execution of more complex tasks.\nThe remarkable capacity of zero-shot in-context learning of these models en-\nables quick adaptation to new tasks without the requirement for labeled data\nfor ﬁne-tuning, which is a critical challenge in ﬁelds like medical informatics[102]\nand robotics[103] where the availability of labeled data is commonly limited or\nnon-existent.\n4 Conclusion"}


created_at: 2026-06-01 06:01:44
topic: persistent://default/default/d94e1968-cc22-40f0-8810-2412728b970e
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{"moddate":"2023-04-05T00:33:07+00:00","keywords":"","chroma:document":"non-existent.\n4 Conclusion\nThis review paper provides a comprehensive survey of ChatGPT and GPT-4,\nhighlighting their potential applications and signiﬁcant contributions to the ﬁeld\nof natural language processing. The ﬁndings of this study reveal that the interest\nin these models is growing rapidly, and they have shown considerable potential\nfor application across a wide range of domains. One key factor contributing\nto the success of ChatGPT and GPT-4 is their ability to perform large-scale\npre-training, which captures knowledge from the vast expanse of the internet,\nallowing the models to learn from a massive amount of data. The integration of\nReinforcement Learning from Human Feedback (RLHF) has further enhanced\nthe model’s adaptability and performance, making it highly eﬃcient in process-\ning natural language. This study has also identiﬁed several potential ethical\nconcerns related to the development and use of ChatGPT and GPT-4. For in-","total_pages":35,"page":25,"title":"","creator":"LaTeX with hyperref","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","subject":"","trapped":"/False","page_label":"26","author":"","creationdate":"2023-04-05T00:33:07+00:00","producer":"pdfTeX-1.40.21"}


created_at: 2026-06-01 06:01:44
topic: persistent://default/default/d94e1968-cc22-40f0-8810-2412728b970e
id: ad4beb7e-ef80-4ebc-8144-9e8516c04723
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{"moddate":"2023-04-05T00:33:07+00:00","page":25,"subject":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","title":"","page_label":"26","producer":"pdfTeX-1.40.21","trapped":"/False","author":"","keywords":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","total_pages":35,"chroma:document":"concerns related to the development and use of ChatGPT and GPT-4. For in-\nstance, there are concerns about the generation of biased or harmful content,\nprivacy violations, and the potential for misuse of the technology. It is crucial\nto address these concerns and ensure that ChatGPT and GPT-4 are developed\nand used in a responsible and ethical manner. Furthermore, the results of this\nstudy demonstrate that there is signiﬁcant potential for ChatGPT and GPT-4\n26","creationdate":"2023-04-05T00:33:07+00:00","creator":"LaTeX with hyperref"}


created_at: 2026-06-01 06:01:44
topic: persistent://default/default/d94e1968-cc22-40f0-8810-2412728b970e
id: 871c5889-18e7-44b2-b924-db069a8119a7
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{"page":26,"chroma:document":"to be applied in a range of domains, including education, history, mathematics,\nphysics, and more. These models can facilitate tasks such as generating sum-\nmaries, answering questions, and providing personalized recommendations to\nusers. Overall, the insights presented in this review paper can serve as a useful\nguide for researchers and practitioners looking to advance the ﬁeld of natural\nlanguage processing. Future research in this ﬁeld should focus on addressing\nethical concerns, exploring new applications, and ensuring the responsible use\nof ChatGPT and GPT-4. The potential of these models to revolutionize natural\nlanguage processing is enormous, and we look forward to seeing more develop-\nments in this ﬁeld.\nReferences\n[1] Radford A, Narasimhan K, Salimans T, Sutskever I, et al. Improving\nlanguage understanding by generative pre-training. OpenAI. 2018.\n[2] Radford A, Wu J, Child R, Luan D, Amodei D, Sutskever I, et al.","keywords":"","moddate":"2023-04-05T00:33:07+00:00","page_label":"27","total_pages":35,"subject":"","trapped":"/False","creator":"LaTeX with hyperref","title":"","producer":"pdfTeX-1.40.21","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","creationdate":"2023-04-05T00:33:07+00:00","author":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf"}


created_at: 2026-06-01 06:01:44
topic: persistent://default/default/d94e1968-cc22-40f0-8810-2412728b970e
id: e0e7227e-17cc-49ad-8c21-4c7e50e0b261
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{"page_label":"27","creator":"LaTeX with hyperref","trapped":"/False","creationdate":"2023-04-05T00:33:07+00:00","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","keywords":"","producer":"pdfTeX-1.40.21","author":"","title":"","total_pages":35,"subject":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","page":26,"chroma:document":"[2] Radford A, Wu J, Child R, Luan D, Amodei D, Sutskever I, et al.\nLanguage models are unsupervised multitask learners. OpenAI blog.\n2019;1(8):9.\n[3] Radford A, Wu J, Amodei D, Amodei D, Clark J, Brundage M, et al. Bet-\nter language models and their implications. OpenAI Blog https://openai\ncom/blog/better-language-models. 2019;1(2).\n[4] Brown T, Mann B, Ryder N, Subbiah M, Kaplan JD, Dhariwal P, et al.\nLanguage models are few-shot learners. Advances in neural information\nprocessing systems. 2020;33:1877-901.\n[5] Ouyang L, Wu J, Jiang X, Almeida D, Wainwright CL, Mishkin P, et al.\nTraining language models to follow instructions with human feedback.\narXiv preprint arXiv:220302155. 2022.\n[6] Christiano PF, Leike J, Brown T, Martic M, Legg S, Amodei D. Deep\nreinforcement learning from human preferences. Advances in neural infor-\nmation processing systems. 2017;30.\n[7] Frieder S, Pinchetti L, Griﬃths RR, Salvatori T, Lukasiewicz T, Pe-","moddate":"2023-04-05T00:33:07+00:00"}


created_at: 2026-06-01 06:01:44
topic: persistent://default/default/d94e1968-cc22-40f0-8810-2412728b970e
id: 6a2aef6b-f9d4-443e-b703-8daacbbfb3e3
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created_at: 2026-06-01 06:01:44
topic: persistent://default/default/d94e1968-cc22-40f0-8810-2412728b970e
id: 16ec4179-200d-4bf0-9118-8304b4dce2f6
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created_at: 2026-06-01 06:01:44
topic: persistent://default/default/d94e1968-cc22-40f0-8810-2412728b970e
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{"source":"./data/Summary of ChatGPTGPT-4 Research.pdf","total_pages":35,"chroma:document":"Importance using ChatGPT. arXiv preprint arXiv:230207142. 2023.\n[17] Wang S, Scells H, Koopman B, Zuccon G. Can chatgpt write a good\nboolean query for systematic review literature search? arXiv preprint\narXiv:230203495. 2023.\n[18] Kuzman T, Mozetic I, Ljubeˇ sic N. ChatGPT: Beginning of an End of\nManual Linguistic Data Annotation? Use Case of Automatic Genre Iden-\ntiﬁcation. arXiv e-prints. 2023:arXiv-2303.\n[19] Amin MM, Cambria E, Schuller BW. Will Aﬀective Computing Emerge\nfrom Foundation Models and General AI? A First Evaluation on Chat-\nGPT. arXiv preprint arXiv:230303186. 2023.\n[20] Zhang B, Ding D, Jing L. How would Stance Detection Techniques Evolve\nafter the Launch of ChatGPT? arXiv preprint arXiv:221214548. 2022.\n[21] Huang F, Kwak H, An J. Is ChatGPT better than Human Annota-\ntors? Potential and Limitations of ChatGPT in Explaining Implicit Hate\nSpeech. arXiv preprint arXiv:230207736. 2023.\n[22] Zhang X, Chowdhury RR, Hong D, Gupta RK, Shang J. Model-","page_label":"28","title":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","moddate":"2023-04-05T00:33:07+00:00","subject":"","keywords":"","creator":"LaTeX with hyperref","creationdate":"2023-04-05T00:33:07+00:00","trapped":"/False","producer":"pdfTeX-1.40.21","page":27,"author":""}


created_at: 2026-06-01 06:01:44
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{"chroma:document":"[22] Zhang X, Chowdhury RR, Hong D, Gupta RK, Shang J. Model-\ning Label Semantics Improves Activity Recognition. arXiv preprint\narXiv:230103462. 2023.\n[23] Fu Q, Teng Z, Georgaklis M, White J, Schmidt DC. NL2CMD: An Up-\ndated Workﬂow for Natural Language to Bash Commands Translation.\narXiv preprint arXiv:230207845. 2023.\n28","moddate":"2023-04-05T00:33:07+00:00","page_label":"28","total_pages":35,"trapped":"/False","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","page":27,"creationdate":"2023-04-05T00:33:07+00:00","creator":"LaTeX with hyperref","producer":"pdfTeX-1.40.21","author":"","subject":"","title":"","keywords":""}


created_at: 2026-06-01 06:01:44
topic: persistent://default/default/d94e1968-cc22-40f0-8810-2412728b970e
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{"chroma:document":"[24] Chen Y, Eger S. Transformers go for the LOLs: Generating (humourous)\ntitles from scientiﬁc abstracts end-to-end. arXiv preprint arXiv:221210522.\n2022.\n[25] Chen N, Wang Y, Jiang H, Cai D, Chen Z, Li J. What would Harry\nsay? Building Dialogue Agents for Characters in a Story. arXiv preprint\narXiv:221106869. 2022.\n[26] Jeblick K, Schachtner B, Dexl J, Mittermeier A, St¨ uber AT, Topalis J,\net al. ChatGPT Makes Medicine Easy to Swallow: An Exploratory Case\nStudy on Simpliﬁed Radiology Reports. arXiv preprint arXiv:221214882.\n2022.\n[27] Xia CS, Zhang L. Conversational automated program repair. arXiv\npreprint arXiv:230113246. 2023.\n[28] Jiao W, ZhaopengTu WJtX. Is ChatGPT A Good Translator? Yes With\nGPT-4 As The Engine.\n[29] Prieto SA, Mengiste ET, de Soto BG. Investigating the Use of\nChatGPT for the Scheduling of Construction Projects. Buildings.\n2023 mar;13(4):857. Available from: https://doi.org/10.3390%\n2Fbuildings13040857.","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","creator":"LaTeX with hyperref","keywords":"","moddate":"2023-04-05T00:33:07+00:00","creationdate":"2023-04-05T00:33:07+00:00","producer":"pdfTeX-1.40.21","author":"","page":28,"page_label":"29","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","title":"","subject":"","total_pages":35,"trapped":"/False"}


created_at: 2026-06-01 06:01:44
topic: persistent://default/default/d94e1968-cc22-40f0-8810-2412728b970e
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created_at: 2026-06-01 06:01:44
topic: persistent://default/default/d94e1968-cc22-40f0-8810-2412728b970e
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{"title":"","creationdate":"2023-04-05T00:33:07+00:00","page":28,"creator":"LaTeX with hyperref","chroma:document":"lenges, Opportunities, and Strategies. arXiv preprint arXiv:221208104.\n2022.\n[35] Khalil M, Er E. Will ChatGPT get you caught? Rethinking of plagiarism\ndetection. arXiv preprint arXiv:230204335. 2023.\n[36] Basic Z, Banovac A, Kruzic I, Jerkovic I. Better by you, better than\nme, chatgpt3 as writing assistance in students essays. arXiv preprint\narXiv:230204536. 2023.\n29","moddate":"2023-04-05T00:33:07+00:00","page_label":"29","producer":"pdfTeX-1.40.21","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","total_pages":35,"author":"","trapped":"/False","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","keywords":"","subject":""}


created_at: 2026-06-01 06:01:44
topic: persistent://default/default/d94e1968-cc22-40f0-8810-2412728b970e
id: 3599ad34-962f-41b0-bee0-e991e2a0ef42
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{"author":"","trapped":"/False","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","moddate":"2023-04-05T00:33:07+00:00","keywords":"","total_pages":35,"chroma:document":"[37] Noever D, Ciolino M. The Turing Deception. arXiv preprint\narXiv:221206721. 2022.\n[38] Megahed FM, Chen YJ, Ferris JA, Knoth S, Jones-Farmer LA. How\nGenerative AI models such as ChatGPT can be (Mis) Used in SPC Prac-\ntice, Education, and Research? An Exploratory Study. arXiv preprint\narXiv:230210916. 2023.\n[39] Treude C. Navigating Complexity in Software Engineering: A Prototype\nfor Comparing GPT-n Solutions. arXiv preprint arXiv:230112169. 2023.\n[40] Sobania D, Briesch M, Hanna C, Petke J. An analysis of the automatic\nbug ﬁxing performance of chatgpt. arXiv preprint arXiv:230108653. 2023.\n[41] Noever D, McKee F. Numeracy from Literacy: Data Science as an Emer-\ngent Skill from Large Language Models. arXiv preprint arXiv:230113382.\n2023.\n[42] McKee F, Noever D. Chatbots in a Botnet World. arXiv preprint\narXiv:221211126. 2022.\n[43] McKee F, Noever D. Chatbots in a Honeypot World. arXiv preprint\narXiv:230103771. 2023.\n[44] Susnjak T. Applying BERT and ChatGPT for Sentiment Analysis of","producer":"pdfTeX-1.40.21","creationdate":"2023-04-05T00:33:07+00:00","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","page":29,"subject":"","page_label":"30","title":"","creator":"LaTeX with hyperref"}


created_at: 2026-06-01 06:01:44
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created_at: 2026-06-01 06:01:44
topic: persistent://default/default/d94e1968-cc22-40f0-8810-2412728b970e
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created_at: 2026-06-01 06:01:44
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created_at: 2026-06-01 06:01:44
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{"creator":"LaTeX with hyperref","page":30,"page_label":"31","trapped":"/False","chroma:document":"[54] Wang Z, Shang J, Liu L, Lu L, Liu J, Han J. Crossweigh: Training named\nentity tagger from imperfect annotations. arXiv preprint arXiv:190901441.\n2019.\n[55] Levow GA. The third international Chinese language processing bakeoﬀ:\nWord segmentation and named entity recognition. In: Proceedings of the\nFifth SIGHAN workshop on Chinese language processing; 2006. p. 108-17.\n[56] Li X, Li F, Pan L, Chen Y, Peng W, Wang Q, et al. DuEE: a large-scale\ndataset for Chinese event extraction in real-world scenarios. In: Natu-\nral Language Processing and Chinese Computing: 9th CCF International\nConference, NLPCC 2020, Zhengzhou, China, October 14–18, 2020, Pro-\nceedings, Part II 9. Springer; 2020. p. 534-45.\n[57] Gormley MR, Yu M, Dredze M. Improved relation extraction with feature-\nrich compositional embedding models. arXiv preprint arXiv:150502419.\n2015.\n[58] Hoﬀmann R, Zhang C, Ling X, Zettlemoyer L, Weld DS. Knowledge-based","creationdate":"2023-04-05T00:33:07+00:00","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","keywords":"","producer":"pdfTeX-1.40.21","subject":"","total_pages":35,"moddate":"2023-04-05T00:33:07+00:00","title":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","author":""}


created_at: 2026-06-01 06:01:44
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created_at: 2026-06-01 06:01:44
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created_at: 2026-06-01 06:01:44
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created_at: 2026-06-01 06:01:44
topic: persistent://default/default/d94e1968-cc22-40f0-8810-2412728b970e
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created_at: 2026-06-01 06:01:44
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created_at: 2026-06-01 06:01:44
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created_at: 2026-06-01 06:01:44
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created_at: 2026-06-01 06:01:44
topic: persistent://default/default/d94e1968-cc22-40f0-8810-2412728b970e
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created_at: 2026-06-01 06:01:44
topic: persistent://default/default/d94e1968-cc22-40f0-8810-2412728b970e
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created_at: 2026-06-01 06:01:44
topic: persistent://default/default/d94e1968-cc22-40f0-8810-2412728b970e
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created_at: 2026-06-01 06:01:44
topic: persistent://default/default/d94e1968-cc22-40f0-8810-2412728b970e
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created_at: 2026-06-01 06:01:51
topic: persistent://default/default/3bd21dcf-6467-4133-ba61-7146653b53cb
id: b1587cbe-8acf-495f-a345-e83ed6f3a5f2
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{"trapped":"/False","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","subject":"","chroma:document":"Summary of ChatGPT/GPT-4 Research\nand Perspective Towards the Future of Large\nLanguage Models\nYiheng Liu ∗1, Tianle Han ∗1, Siyuan Ma 1, Jiayue Zhang 1,\nYuanyuan Yang1, Jiaming Tian 1, Hao He 1, Antong Li 2, Mengshen\nHe1, Zhengliang Liu 3, Zihao Wu 3, Dajiang Zhu 4, Xiang Li 5, Ning\nQiang1, Dingang Shen 6,7,8, Tianming Liu 3, and Bao Ge †1\n1School of Physics and Information Technology, Shaanxi Normal University, Xi’an\n710119 China\n2School of Life and Technology Biomedical-Engineering, Xi’an Jiaotong University,\nXi’an 710049, China\n3School of Computing, The University of Georgia, Athens 30602, USA\n4Department of Computer Science and Engineering, The University of Texas at\nArlington, Arlington 76019, USA\n5Department of Radiology, Massachusetts General Hospital and Harvard Medical\nSchool, Boston 02115, USA\n6School of Biomedical Engineering, ShanghaiTech University, Shanghai 201210,\nChina\n7Shanghai United Imaging Intelligence Co., Ltd., Shanghai 200230, China","keywords":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","page_label":"1","creator":"LaTeX with hyperref","producer":"pdfTeX-1.40.21","moddate":"2023-04-05T00:33:07+00:00","total_pages":35,"page":0,"creationdate":"2023-04-05T00:33:07+00:00","title":"","author":""}


created_at: 2026-06-01 06:01:51
topic: persistent://default/default/3bd21dcf-6467-4133-ba61-7146653b53cb
id: e28d53f5-d3a0-4e17-a60d-c43ba1d973e3
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{"page":0,"moddate":"2023-04-05T00:33:07+00:00","creator":"LaTeX with hyperref","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","producer":"pdfTeX-1.40.21","author":"","page_label":"1","chroma:document":"China\n7Shanghai United Imaging Intelligence Co., Ltd., Shanghai 200230, China\n8Shanghai Clinical Research and Trial Center, Shanghai 201210, China\nAbstract\nThis paper presents a comprehensive survey of ChatGPT and GPT-4,\nstate-of-the-art large language models (LLM) from the GPT series, and\ntheir prospective applications across diverse domains. Indeed, key innova-\ntions such as large-scale pre-training that captures knowledge across the\nentire world wide web, instruction ﬁne-tuning and Reinforcement Learn-\ning from Human Feedback (RLHF) have played signiﬁcant roles in en-\nhancing LLMs’ adaptability and performance. We performed an in-depth\nanalysis of 194 relevant papers on arXiv, encompassing trend analysis,\nword cloud representation, and distribution analysis across various appli-\ncation domains. The ﬁndings reveal a signiﬁcant and increasing interest\nin ChatGPT/GPT-4 research, predominantly centered on direct natural","keywords":"","total_pages":35,"title":"","subject":"","creationdate":"2023-04-05T00:33:07+00:00","trapped":"/False"}


created_at: 2026-06-01 06:01:51
topic: persistent://default/default/3bd21dcf-6467-4133-ba61-7146653b53cb
id: cbf02128-b11a-4f8d-9338-0d37c3cefebf
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{"keywords":"","page_label":"1","creator":"LaTeX with hyperref","producer":"pdfTeX-1.40.21","moddate":"2023-04-05T00:33:07+00:00","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","page":0,"source":"./data/Summary of ChatGPTGPT-4 Research.pdf","author":"","title":"","trapped":"/False","chroma:document":"in ChatGPT/GPT-4 research, predominantly centered on direct natural\nlanguage processing applications, while also demonstrating considerable\npotential in areas ranging from education and history to mathematics,\n∗Co-ﬁrst author\n†Corresponding author: bob ge@snnu.edu.cn\n1\narXiv:2304.01852v1  [cs.CL]  4 Apr 2023","total_pages":35,"subject":"","creationdate":"2023-04-05T00:33:07+00:00"}


created_at: 2026-06-01 06:01:51
topic: persistent://default/default/3bd21dcf-6467-4133-ba61-7146653b53cb
id: 1cfd68e3-8280-4f4c-abe9-f0d206d6f207
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{"total_pages":35,"author":"","chroma:document":"medicine, and physics. This study endeavors to furnish insights into Chat-\nGPT’s capabilities, potential implications, ethical concerns, and oﬀer di-\nrection for future advancements in this ﬁeld.\n1 Introduction\nRecent advances in natural language processing (NLP) have led to the devel-\nopment of powerful language models such as the GPT (Generative Pre-trained\nTransformer) series [1, 2, 3, 4, 5], including large language models (LLM) such\nas ChatGPT and GPT-4. These models are pre-trained on vast amounts of\ntext data and have demonstrated exceptional performance in a wide range of\nNLP tasks, including language translation, text summarization, and question-\nanswering. In particular, the ChatGPT model has demonstrated its potential\nin various ﬁelds, including education, healthcare, reasoning, text generation,\nhuman-machine interaction, and scientiﬁc research.\nA key milestone of LLM development is InstructGPT [5], a framework that","creator":"LaTeX with hyperref","subject":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","producer":"pdfTeX-1.40.21","page":1,"title":"","page_label":"2","trapped":"/False","creationdate":"2023-04-05T00:33:07+00:00","keywords":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","moddate":"2023-04-05T00:33:07+00:00"}


created_at: 2026-06-01 06:01:51
topic: persistent://default/default/3bd21dcf-6467-4133-ba61-7146653b53cb
id: 019b00ef-92a2-426c-b3a8-4105d6a2bdd2
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{"page_label":"2","creator":"LaTeX with hyperref","subject":"","chroma:document":"A key milestone of LLM development is InstructGPT [5], a framework that\nallows for instruction ﬁne-tuning of a pre-trained language model based on Re-\ninforcement Learning from Human Feedback (RLHF) [6, 5]. This framework\nenables an LLM to adapt to a wide range of NLP tasks, making it highly ver-\nsatile and ﬂexible by leveraging human feedback. RLHF enables the model to\nalign with human preferences and human values, which signiﬁcantly improves\nfrom large language models that are solely trained text corpora through unsu-\npervised pre-training. ChatGPT is a successor to InstructGPT. Since its release\nin December 2022, ChatGPT has been equipped with these advanced develop-\nments, leading to impressive performances in various downstream NLP tasks\nsuch as reasoning and generalized text generation. These unprecedented NLP\ncapabilities spur applications in diverse domains such as education, healthcare,\nhuman-machine interaction, medicine and scientiﬁc research. ChatGPT has re-","producer":"pdfTeX-1.40.21","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","keywords":"","author":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","total_pages":35,"trapped":"/False","moddate":"2023-04-05T00:33:07+00:00","title":"","creationdate":"2023-04-05T00:33:07+00:00","page":1}


created_at: 2026-06-01 06:01:51
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{"total_pages":35,"author":"","page_label":"2","producer":"pdfTeX-1.40.21","chroma:document":"human-machine interaction, medicine and scientiﬁc research. ChatGPT has re-\nceived widespread attention and interest, leading to an increasing number of\napplications and research that harness its exceeding potential. The open release\nof the multi-modal GPT-4 model further expands the horizon of large language\nmodels and empowers exciting developments that involve diverse data beyond\ntext.\nThe purpose of this paper is to provide a comprehensive survey of the existing\nresearch on ChatGPT and its potential applications in various ﬁelds. To achieve\nthis goal, we conducted a thorough analysis of papers related to ChatGPT in\nthe arXiv repository. As of April 1st, 2023, there are a total of 194 papers\nmentioning ChatGPT on arXiv. In this study, we conducted a trend analysis of\nthese papers and generated a word cloud to visualize the commonly used terms.\nAdditionally, we also examined the distribution of the papers across various","page":1,"title":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","keywords":"","subject":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","trapped":"/False","creationdate":"2023-04-05T00:33:07+00:00","moddate":"2023-04-05T00:33:07+00:00","creator":"LaTeX with hyperref"}


created_at: 2026-06-01 06:01:51
topic: persistent://default/default/3bd21dcf-6467-4133-ba61-7146653b53cb
id: f4a0794c-7b69-4298-ba2d-cf3cf8e7acf7
encoding: FLOAT32
metadata:
{"subject":"","page_label":"2","keywords":"","author":"","creator":"LaTeX with hyperref","moddate":"2023-04-05T00:33:07+00:00","title":"","creationdate":"2023-04-05T00:33:07+00:00","trapped":"/False","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","producer":"pdfTeX-1.40.21","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","total_pages":35,"chroma:document":"Additionally, we also examined the distribution of the papers across various\nﬁelds and presented the corresponding statistics. Figure 1 displays the daily\nsubmission trend of papers related to ChatGPT, indicating a growing interest\nin this ﬁeld. Figure 2 illustrates the word cloud analysis of all the papers. We\ncan observe that the current research is primarily focused on natural language\nprocessing, but there is still signiﬁcant potential for research in other ﬁelds such\n2","page":1}


created_at: 2026-06-01 06:01:51
topic: persistent://default/default/3bd21dcf-6467-4133-ba61-7146653b53cb
id: 602f140b-aa79-4a89-920d-3608d0ea7be9
encoding: FLOAT32
metadata:
{"trapped":"/False","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","page":2,"creationdate":"2023-04-05T00:33:07+00:00","title":"","total_pages":35,"page_label":"3","creator":"LaTeX with hyperref","author":"","moddate":"2023-04-05T00:33:07+00:00","producer":"pdfTeX-1.40.21","keywords":"","chroma:document":"Figure 1: The number of papers submitted by researchers per day.\n3","subject":""}


created_at: 2026-06-01 06:01:51
topic: persistent://default/default/3bd21dcf-6467-4133-ba61-7146653b53cb
id: ee1f82c6-9a1a-4b79-9320-9c4e6c348b66
encoding: FLOAT32
metadata:
{"creator":"LaTeX with hyperref","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","creationdate":"2023-04-05T00:33:07+00:00","subject":"","page":3,"author":"","total_pages":35,"chroma:document":"Figure 2: Word cloud analysis of all the 194 papers.\nas education and history. This is further supported by Figure 3, which displays\nthe distribution of submitted papers across various ﬁelds, highlighting the need\nfor more research and development in these areas.\nThis paper aims to shed light on the promising capabilities of ChatGPT\nand provide insight into its potential impact in the future, including ethical\nconsiderations. Through this survey, we hope to provide insights into how these\nmodels can be improved and extended in the future. In section 2, we will\nreview the existing work related to ChatGPT, including its applications, ethical\nconsiderations, and evaluation. In addition to discussing the current state of\nresearch related to ChatGPT, we will also explore its limitations in section 3.\nFurthermore, we will provide guidance on future directions for language model\ndevelopment.\n2 Related work of ChatGPT","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","page_label":"4","trapped":"/False","keywords":"","moddate":"2023-04-05T00:33:07+00:00","title":"","producer":"pdfTeX-1.40.21"}


created_at: 2026-06-01 06:01:51
topic: persistent://default/default/3bd21dcf-6467-4133-ba61-7146653b53cb
id: 3bce404f-76e0-4f87-b989-e4a5b541d40d
encoding: FLOAT32
metadata:
{"trapped":"/False","creationdate":"2023-04-05T00:33:07+00:00","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","subject":"","producer":"pdfTeX-1.40.21","author":"","keywords":"","total_pages":35,"page":3,"title":"","moddate":"2023-04-05T00:33:07+00:00","creator":"LaTeX with hyperref","page_label":"4","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","chroma:document":"development.\n2 Related work of ChatGPT\nIn this section, we review the latest research related to the application, ethics,\nand evaluation of ChatGPT.\n2.1 Application of ChatGPT\n2.1.1 Question And Answering\nIn the education ﬁeld\nChatGPT is commonly used for question and answers testing in the edu-\ncation sector. Users can use ChatGPT to learn, compare and verify answers\nfor diﬀerent academic subjects such as physics, mathematics, and chemistry,\n4"}


created_at: 2026-06-01 06:01:51
topic: persistent://default/default/3bd21dcf-6467-4133-ba61-7146653b53cb
id: c827048c-db50-48aa-b17f-f2813f9524ac
encoding: FLOAT32
metadata:
{"creator":"LaTeX with hyperref","creationdate":"2023-04-05T00:33:07+00:00","producer":"pdfTeX-1.40.21","keywords":"","page":4,"title":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","trapped":"/False","subject":"","chroma:document":"Figure 3: The distribution of submitted papers across various ﬁelds.\n5","moddate":"2023-04-05T00:33:07+00:00","total_pages":35,"author":"","page_label":"5"}


created_at: 2026-06-01 06:01:51
topic: persistent://default/default/3bd21dcf-6467-4133-ba61-7146653b53cb
id: bf6b60da-47b0-4470-ae0d-0e41c6e1e139
encoding: FLOAT32
metadata:
{"keywords":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","trapped":"/False","producer":"pdfTeX-1.40.21","creator":"LaTeX with hyperref","total_pages":35,"chroma:document":"and/or conceptual subjects such as philosophy and religion. Additionally, users\ncan ask open-ended and analytical questions to understand the capabilities of\nChatGPT.\nIn the ﬁeld of mathematics, Frieder et al. [7] constructed the GHOSTS nat-\nural language dataset, which consists of graduate-level math test questions.\nThe authors tested ChatGPT’s math abilities on the GHOSTS dataset us-\ning a question-and-answer format and evaluated it according to ﬁne-grained\nstandards.In the Grad Text dataset, which covers simple set theory and logic\nproblems, ChatGPT performed the best. However, in the Olympiad-Problem-\nSolving dataset, ChatGPT performed poorly, receiving only two 4-point scores\n(out of a total of 5), with the majority of scores being 2 points. In the Holes-\nin-Proofs dataset, ChatGPT received the lowest score of 1 point. In the MATH\ndataset, ChatGPT only scored impressively in 26% of cases. These results sug-","author":"","page":5,"creationdate":"2023-04-05T00:33:07+00:00","subject":"","page_label":"6","moddate":"2023-04-05T00:33:07+00:00","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","title":""}


created_at: 2026-06-01 06:01:51
topic: persistent://default/default/3bd21dcf-6467-4133-ba61-7146653b53cb
id: 5203e665-7ae9-4ec2-93f6-19311f8031f5
encoding: FLOAT32
metadata:
{"page_label":"6","creator":"LaTeX with hyperref","page":5,"producer":"pdfTeX-1.40.21","author":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","total_pages":35,"title":"","moddate":"2023-04-05T00:33:07+00:00","subject":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","creationdate":"2023-04-05T00:33:07+00:00","keywords":"","chroma:document":"dataset, ChatGPT only scored impressively in 26% of cases. These results sug-\ngest that ChatGPT’s math abilities are clearly lower than those of ordinary\nmath graduate students. Although ChatGPT can generally understand math\nproblems, it fails to provide the correct solutions. Pardos et al. [8] used the\nOpen Adaptive Tutoring system (OATutor) to investigate whether prompts gen-\nerated by ChatGPT were helpful for learning algebra, with 77 participants from\nMechanical Turk taking part in the experiment. The experiment used ques-\ntions from OpenStax’s Elementary and Intermediate Algebra textbooks. These\nparticipants were randomly assigned to either a control group (with manual\nprompts) or an experimental group (with ChatGPT prompts). For each ques-\ntion in both courses, the authors obtained answers from ChatGPT through a\nquestion-and-answer format and evaluated scores according to three criteria:\nChatGPT provided an answer, the answer was correct, and inappropriate lan-","trapped":"/False"}


created_at: 2026-06-01 06:01:51
topic: persistent://default/default/3bd21dcf-6467-4133-ba61-7146653b53cb
id: 3890f23d-d581-4219-a10a-edecdd23e7c3
encoding: FLOAT32
metadata:
{"source":"./data/Summary of ChatGPTGPT-4 Research.pdf","producer":"pdfTeX-1.40.21","page":5,"author":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","creationdate":"2023-04-05T00:33:07+00:00","creator":"LaTeX with hyperref","subject":"","chroma:document":"ChatGPT provided an answer, the answer was correct, and inappropriate lan-\nguage was not used in the answer. The study found that 70% of prompts gener-\nated by ChatGPT passed manual quality checks, and both humans and Chat-\nGPT produced positive learning gains. However, the scores of human prompts\nranged from 74.59% to 84.32%, signiﬁcantly higher than those of ChatGPT\nprompts. Shakarian et al. [9] studied the performance of ChatGPT on math\nword problems (MWPs), using the DRAW-1K dataset for experimentation. The\ndataset consists of 1000 MWPs and their answers, along with algebraic equa-\ntion templates for solving such problems. The authors used the idea of machine\nlearning introspection and built performance prediction models using random\nforests and XGBoost, and evaluated them on the dataset using ﬁve-fold cross-\nvalidation. ChatGPT’s accuracy increased from an initial 34% to a ﬁnal 69%,\nwhile its recall increased from an initial 41% to a ﬁnal 83%. The authors also","page_label":"6","title":"","keywords":"","total_pages":35,"trapped":"/False","moddate":"2023-04-05T00:33:07+00:00"}


created_at: 2026-06-01 06:01:51
topic: persistent://default/default/3bd21dcf-6467-4133-ba61-7146653b53cb
id: 61e1e8e2-2d48-4bec-a085-f542f5495e09
encoding: FLOAT32
metadata:
{"ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","keywords":"","subject":"","page":5,"author":"","creator":"LaTeX with hyperref","page_label":"6","producer":"pdfTeX-1.40.21","creationdate":"2023-04-05T00:33:07+00:00","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","trapped":"/False","total_pages":35,"chroma:document":"while its recall increased from an initial 41% to a ﬁnal 83%. The authors also\nfound that ChatGPT’s failure rate decreased from an initial 84% to a ﬁnal\n20%, indicating that performance can vary greatly depending on speciﬁc job\nrequirements.\nIn the ﬁeld of physics, Lehnert et al. [10] explored the capabilities and limita-\ntions of ChatGPT by studying how it handles obscure physics topics such as the\nswamp land conjecture in string theory. The experimental dialogue began with\nbroader and more general questions in the ﬁeld of string theory before narrowing\ndown to speciﬁc swamp land conjectures and examining ChatGPT’s understand-\ning of them. The study found that ChatGPT could deﬁne and explain diﬀerent\n6","moddate":"2023-04-05T00:33:07+00:00","title":""}


created_at: 2026-06-01 06:01:51
topic: persistent://default/default/3bd21dcf-6467-4133-ba61-7146653b53cb
id: f3c0571f-e0e7-4ae9-9000-de4f36cc3691
encoding: FLOAT32
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{"total_pages":35,"ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","page_label":"7","keywords":"","trapped":"/False","creationdate":"2023-04-05T00:33:07+00:00","producer":"pdfTeX-1.40.21","author":"","subject":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","creator":"LaTeX with hyperref","moddate":"2023-04-05T00:33:07+00:00","title":"","chroma:document":"concepts in various styles, but was not eﬀective in truly connecting various con-\ncepts. It would conﬁdently provide false information and fabricate statements\nwhen necessary, indicating that ChatGPT cannot truly create new knowledge\nor establish new connections. However, in terms of identifying analogies and\ndescribing abstract concepts of visual representation, ChatGPT can cleverly\nuse language. Kortemeyer et al. [11] evaluated ChatGPT’s ability to answer\ncalculus-based physics questions through a question-and-answer test. The tests\nincluded online homework, clicker questions, programming exercises, and exams\ncovering classical mechanics, thermodynamics, electricity and magnetism, and\nmodern physics. While ChatGPT was able to pass the course, it also demon-\nstrated many misconceptions and errors commonly held by beginners. West et\nal. [12] used the Force Concept Inventory (FCI) to evaluate ChatGPT’s accu-\nracy in answering physics concept problems related to kinematics and Newtonian","page":6}


created_at: 2026-06-01 06:01:51
topic: persistent://default/default/3bd21dcf-6467-4133-ba61-7146653b53cb
id: 04cde3b3-6436-4fab-9dc9-adea39747400
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metadata:
{"page":6,"page_label":"7","creationdate":"2023-04-05T00:33:07+00:00","subject":"","author":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","creator":"LaTeX with hyperref","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","title":"","chroma:document":"racy in answering physics concept problems related to kinematics and Newtonian\nmechanics in the ﬁrst semester of college physics. The FCI covers topics such\nas kinematics, projectile motion, free fall, circular motion, and Newton’s laws.\nThe study included data from 415 students who took the FCI at the end of the\nsemester, with an average score of 56%, while ChatGPT scored approximately\nbetween 50% to 65%. The authors demonstrated that ChatGPT’s performance\nin physics learning can reach or even exceed the average level of a semester of\ncollege physics.\nIn the medical ﬁeld\nChatGPT’s question-answering capabilities can also be applied in the med-\nical ﬁeld, such as for answering medical questions from patients or assisting\nhealthcare professionals in diagnosing diseases. Nov et al. [13] evaluated the fea-\nsibility of using ChatGPT for patient-doctor communication. The experiment\nextracted 10 representative patient-doctor interactions from EHR, placed the","trapped":"/False","moddate":"2023-04-05T00:33:07+00:00","producer":"pdfTeX-1.40.21","total_pages":35,"keywords":""}


created_at: 2026-06-01 06:01:51
topic: persistent://default/default/3bd21dcf-6467-4133-ba61-7146653b53cb
id: 52baadee-9514-45f3-866c-6ae85028f2f1
encoding: FLOAT32
metadata:
{"page":6,"title":"","moddate":"2023-04-05T00:33:07+00:00","subject":"","chroma:document":"extracted 10 representative patient-doctor interactions from EHR, placed the\npatient’s questions in ChatGPT, and asked ChatGPT to respond using roughly\nthe same number of words as the doctor’s response. Each patient’s question was\nanswered by either the doctor or ChatGPT, and the patient was informed that\n5 were answered by the doctor and 5 were generated by ChatGPT, and was\nasked to correctly identify the source of the response. The results of the exper-\niment showed that the probability of correctly identifying ChatGPT’s response\nwas 65.5%, while the probability of correctly identifying the doctor’s response\nwas 65.1%. In addition, the experiment found that the patient’s response to\nthe trustworthiness of ChatGPT’s function was weakly positive (average Likert\nscore: 3.4), and trust decreased as the complexity of health-related tasks in\nthe questions increased. ChatGPT’s responses to patient questions were only","author":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","page_label":"7","creationdate":"2023-04-05T00:33:07+00:00","trapped":"/False","total_pages":35,"keywords":"","producer":"pdfTeX-1.40.21","creator":"LaTeX with hyperref"}


created_at: 2026-06-01 06:01:51
topic: persistent://default/default/3bd21dcf-6467-4133-ba61-7146653b53cb
id: 816463f5-f3e3-40d7-ade4-b7bcfe37280b
encoding: FLOAT32
metadata:
{"moddate":"2023-04-05T00:33:07+00:00","page":6,"author":"","creationdate":"2023-04-05T00:33:07+00:00","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","title":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","trapped":"/False","total_pages":35,"creator":"LaTeX with hyperref","keywords":"","producer":"pdfTeX-1.40.21","subject":"","page_label":"7","chroma:document":"the questions increased. ChatGPT’s responses to patient questions were only\nslightly diﬀerent from those of doctors, but people seem to trust ChatGPT to\nanswer low-risk health questions, while for complex medical questions, people\nstill tend to trust the doctor’s responses and advice.\nTu et al. [14] explored the causal discovery ability of ChatGPT in the diag-\nnosis of neuropathic pain. Causal relationship discovery aims to reveal potential\nunknown causal relationships based purely on observed data [15]. The experi-\nmental results found that ChatGPT has some limitations in understanding new\n7"}


created_at: 2026-06-01 06:01:51
topic: persistent://default/default/3bd21dcf-6467-4133-ba61-7146653b53cb
id: 58118d09-807a-4a87-b0c1-1d56fc6bab1d
encoding: FLOAT32
metadata:
{"page_label":"8","total_pages":35,"ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","author":"","page":7,"subject":"","title":"","trapped":"/False","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","chroma:document":"knowledge and concepts beyond the existing textual training data corpus, that\nis, it only understands language commonly used to describe situations and not\nunderlying knowledge. In addition, its performance consistency and stability are\nnot high, as the experiment observed that it would provide diﬀerent answers for\nthe same question under multiple inquiries. However, despite the many limita-\ntions of ChatGPT, we believe that it has a great opportunity to improve causal\nrelationship research.\nIn the other ﬁeld\nGuo et al. [16] attempted to apply ChatGPT in the ﬁeld of communication,\nspeciﬁcally using ChatGPT for ordered importance semantic communication,\nwhere ChatGPT plays the role of an intelligent consulting assistant that can\nreplace humans in identifying the semantic importance of words in messages and\ncan be directly embedded into the current communication system. For a message\nto be transmitted, the sender ﬁrst utilizes ChatGPT to output the semantic","producer":"pdfTeX-1.40.21","moddate":"2023-04-05T00:33:07+00:00","creationdate":"2023-04-05T00:33:07+00:00","keywords":"","creator":"LaTeX with hyperref"}


created_at: 2026-06-01 06:01:51
topic: persistent://default/default/3bd21dcf-6467-4133-ba61-7146653b53cb
id: 2699db0e-25a6-4c4a-9c71-38a770bcbd43
encoding: FLOAT32
metadata:
{"chroma:document":"to be transmitted, the sender ﬁrst utilizes ChatGPT to output the semantic\nimportance order of each word. Then, the transmitter executes an unequal error\nprotection transmission strategy based on the importance order to make the\ntransmission of important words in the message more reliable. The experimental\nresults show that the error rate and semantic loss of important words measured\nin the communication system embedded with ChatGPT are much lower than\nthose of existing communication schemes, indicating that ChatGPT can protect\nimportant words well and make semantic communication more reliable.\nWang et al. [17] studied the eﬀectiveness of ChatGPT in generating high-\nquality Boolean queries for systematic literature search. They designed a wide\nrange of prompts and investigated these tasks on more than 100 systematic\nreview topics. In the end, queries generated by ChatGPT achieved higher accu-\nracy compared to the currently most advanced query generation methods but","subject":"","producer":"pdfTeX-1.40.21","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","page_label":"8","title":"","creator":"LaTeX with hyperref","trapped":"/False","moddate":"2023-04-05T00:33:07+00:00","total_pages":35,"source":"./data/Summary of ChatGPTGPT-4 Research.pdf","author":"","creationdate":"2023-04-05T00:33:07+00:00","keywords":"","page":7}


created_at: 2026-06-01 06:01:51
topic: persistent://default/default/3bd21dcf-6467-4133-ba61-7146653b53cb
id: 2c08b908-e535-41fe-b9f0-8d47e325dda9
encoding: FLOAT32
metadata:
{"author":"","page_label":"8","page":7,"keywords":"","chroma:document":"racy compared to the currently most advanced query generation methods but\nat the cost of reduced recall. For time-limited rapid reviews, it is often accept-\nable to trade oﬀ higher precision for lower recall. Additionally, ChatGPT can\ngenerate high search accuracy Boolean queries by guiding the prompts. How-\never, it should be noted that when two queries use the same prompts, ChatGPT\ngenerates diﬀerent queries, indicating its limitations in consistency and stabil-\nity. Overall, this study demonstrated the potential of ChatGPT in generating\neﬀective Boolean queries for systematic literature searches.\n2.1.2 Text Classiﬁcation\nThe purpose of text classiﬁcation is to assign text data to predeﬁned categories.\nThis task is critical for many applications, including sentiment analysis, spam\ndetection, and topic modeling. While traditional machine learning algorithms\nhave been widely used for text classiﬁcation, recent advances in natural lan-","trapped":"/False","creationdate":"2023-04-05T00:33:07+00:00","producer":"pdfTeX-1.40.21","subject":"","moddate":"2023-04-05T00:33:07+00:00","title":"","total_pages":35,"ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","creator":"LaTeX with hyperref"}


created_at: 2026-06-01 06:01:51
topic: persistent://default/default/3bd21dcf-6467-4133-ba61-7146653b53cb
id: b5bde075-bf40-471a-9216-76bddbf88df6
encoding: FLOAT32
metadata:
{"total_pages":35,"page":7,"producer":"pdfTeX-1.40.21","chroma:document":"have been widely used for text classiﬁcation, recent advances in natural lan-\nguage processing have led to the development of more advanced techniques.\nChatGPT has shown immense potential in this ﬁeld. Its ability to accurately\nclassify text, ﬂexibility in handling various classiﬁcation tasks, and potential for\ncustomization make it a valuable tool for text classiﬁcation, as evidenced by\nseveral studies in the literature.\n8","trapped":"/False","creationdate":"2023-04-05T00:33:07+00:00","creator":"LaTeX with hyperref","page_label":"8","subject":"","keywords":"","moddate":"2023-04-05T00:33:07+00:00","title":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","author":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2"}


created_at: 2026-06-01 06:01:51
topic: persistent://default/default/3bd21dcf-6467-4133-ba61-7146653b53cb
id: 40a305d7-1000-4ab8-959f-0a49b57db085
encoding: FLOAT32
metadata:
{"keywords":"","subject":"","trapped":"/False","creator":"LaTeX with hyperref","producer":"pdfTeX-1.40.21","title":"","author":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","creationdate":"2023-04-05T00:33:07+00:00","moddate":"2023-04-05T00:33:07+00:00","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","page":8,"page_label":"9","chroma:document":"Kuzman et al. [18] employed ChatGPT for automatic genre recognition, with\nthe goal of simplifying the text classiﬁcation task by utilizing ChatGPT’s zero-\nshot classiﬁcation capability. They compared ChatGPT’s genre recognition per-\nformance, using two prompt languages (EN and SL), with the X-GENRE clas-\nsiﬁer based on the multilingual model XLM-RoBERTa on the English dataset\nEN-GINCO and the Slovenian dataset GINCO. The results showed that when\nEN was used as the prompt language, ChatGPT achieved Micro F1, Macro F1,\nand Accuracy scores of 0.74, 0.66, and 0.72. However, on the GINCO dataset,\nChatGPT’s genre recognition performance with both EN and SL prompt lan-\nguages was lower than that of the X-GENRE classiﬁer to varying degrees.\nAmin et al. [19] evaluated the text classiﬁcation ability of ChatGPT in\naﬀective computing by using it to perform personality prediction, sentiment\nanalysis, and suicide ideation detection tasks. They prompted ChatGPT with","total_pages":35}


created_at: 2026-06-01 06:01:51
topic: persistent://default/default/3bd21dcf-6467-4133-ba61-7146653b53cb
id: 6d3d2fb2-7be6-4e60-a44e-c48c6108236b
encoding: FLOAT32
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{"ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","producer":"pdfTeX-1.40.21","trapped":"/False","creationdate":"2023-04-05T00:33:07+00:00","page":8,"title":"","author":"","page_label":"9","keywords":"","creator":"LaTeX with hyperref","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","chroma:document":"analysis, and suicide ideation detection tasks. They prompted ChatGPT with\ncorresponding prompts on three datasets: First Impressions, Sentiment140, and\nSuicide and Depression, and compared its classiﬁcation performance with three\nbaseline models: RoBERTa-base, Word2Vec, and BoW. The results showed\nthat ChatGPT’s accuracy and UAR for the ﬁve personality classiﬁcations on\nthe First Impressions dataset were lower than the baseline methods to varying\ndegrees. On the Sentiment140 dataset, ChatGPT’s accuracy and UAR were 85.5\nand 85.5, respectively, which were better than the three baseline methods. On\nthe Suicide and Depression dataset, ChatGPT’s accuracy and UAR were 92.7\nand 91.2, respectively, which were lower than RoBERTa, the best-performing\nbaseline method.\nZhang et al. [20] employed ChatGPT for stance detection, which includes\nsupport and opposition. They used ChatGPT to classify the political stance\nof tweets in the SemEval-2016 and P-Stance datasets. SemEval-2016 contains","total_pages":35,"moddate":"2023-04-05T00:33:07+00:00","subject":""}


created_at: 2026-06-01 06:01:51
topic: persistent://default/default/3bd21dcf-6467-4133-ba61-7146653b53cb
id: 269c573f-47e0-4928-b597-0747ae296519
encoding: FLOAT32
metadata:
{"total_pages":35,"moddate":"2023-04-05T00:33:07+00:00","subject":"","page":8,"page_label":"9","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","producer":"pdfTeX-1.40.21","chroma:document":"of tweets in the SemEval-2016 and P-Stance datasets. SemEval-2016 contains\n4,870 English tweets, and they selected tweets with the most commonly occur-\nring FM, LA, and HC political labels for stance classiﬁcation. The P-Stance\ndataset has 21,574 English tweets, and they classiﬁed the stance of tweets to-\nwards Trump, Biden, and Bernie. The ﬁnal results showed that on the SemEval-\n2016 dataset, ChatGPT achieved F1-m scores of 68.4, 58.2, and 79.5 for the FM,\nLA, and HC political labels, and F1-avg scores of 72.6, 59.3, and 78.0, respec-\ntively. On the P-Stance dataset, ChatGPT achieved F1-m scores of 82.8, 82.3,\nand 79.4 for the Trump, Biden, and Bernie political ﬁgures, and F1-avg scores\nof 83.2, 82.0, and 79.4, respectively.\nHuang et al. [21] used ChatGPT to detect implicit hate speech in tweets.\nThey selected 12.5% (795 tweets) of the LatentHatred dataset containing im-\nplicit hate speech and asked ChatGPT to classify them into three categories:","creator":"LaTeX with hyperref","trapped":"/False","keywords":"","creationdate":"2023-04-05T00:33:07+00:00","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","author":"","title":""}


created_at: 2026-06-01 06:01:51
topic: persistent://default/default/3bd21dcf-6467-4133-ba61-7146653b53cb
id: 84d4b86a-6be2-40a5-993c-693598c2476e
encoding: FLOAT32
metadata:
{"total_pages":35,"page":8,"keywords":"","moddate":"2023-04-05T00:33:07+00:00","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","trapped":"/False","page_label":"9","author":"","title":"","subject":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","producer":"pdfTeX-1.40.21","chroma:document":"plicit hate speech and asked ChatGPT to classify them into three categories:\nimplicit hate speech, non-hate speech, and uncertain. The results showed that\nChatGPT correctly recognized 636 (80%) of the tweets. The number of tweets\nclassiﬁed as non-hate speech and uncertain were 146 (18.4%) and 13 (1.6%),\nrespectively. The results of the reclassiﬁcation of tweets in the non-hate speech\nand uncertain categories by Amazon Mechanical Turk (Mturk) workers were\nconsistent with ChatGPT’s classiﬁcation.\nOverall, ChatGPT has tremendous potential in text classiﬁcation tasks, as it\ncan eﬀectively address problems such as genre identiﬁcation, sentiment analysis,\n9","creationdate":"2023-04-05T00:33:07+00:00","creator":"LaTeX with hyperref"}


created_at: 2026-06-01 06:01:51
topic: persistent://default/default/3bd21dcf-6467-4133-ba61-7146653b53cb
id: 93b124cd-88ec-4eb5-9b62-021c27f6bbf5
encoding: FLOAT32
metadata:
{"creator":"LaTeX with hyperref","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","page":9,"author":"","title":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","subject":"","moddate":"2023-04-05T00:33:07+00:00","page_label":"10","chroma:document":"stance detection, and more. However, there are still challenges that ChatGPT\nfaces in the ﬁeld of text classiﬁcation. Firstly, it struggles to perform well in\nclassiﬁcation tasks with rare or out-of-vocabulary words since it heavily relies\non the distribution of training data. Additionally, the signiﬁcant computational\nresources required for training and utilizing ChatGPT can limit its use in some\napplications.\n2.1.3 Text generation\nWe live in an era of information explosion, and text is an eﬃcient way of trans-\nmitting information. The diversity of information has led to a diversity of text\ncategories. When researchers use ChatGPT’s text generation capabilities for\nresearch, they inevitably choose to generate diﬀerent types of text. In the pro-\ncess of reading papers, we found that the word count of the text generated by\nresearchers increased from small to large, so we wanted to summarize existing\nresearch based on the size of the text word count. We divided the generated","trapped":"/False","producer":"pdfTeX-1.40.21","creationdate":"2023-04-05T00:33:07+00:00","keywords":"","total_pages":35}


created_at: 2026-06-01 06:01:51
topic: persistent://default/default/3bd21dcf-6467-4133-ba61-7146653b53cb
id: cc2d1d18-e1c9-460f-bfc1-c0eb8ae0dceb
encoding: FLOAT32
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{"producer":"pdfTeX-1.40.21","trapped":"/False","page":9,"creationdate":"2023-04-05T00:33:07+00:00","total_pages":35,"ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","title":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","author":"","creator":"LaTeX with hyperref","keywords":"","moddate":"2023-04-05T00:33:07+00:00","chroma:document":"research based on the size of the text word count. We divided the generated\ntext into three levels: phrases, sentences, and paragraphs.\nThe following article uses ChatGPT to generate phrases. Zhang et al. [22]\nproves that the semantic HAR model with semantic augmentation added dur-\ning training performs better in motion recognition than other models. Semantic\naugmentation requires shared tokens, which is lacking in some datasets. There-\nfore, authors leverage ChatGPT for an automated label generation approach\nfor datasets originally without shared tokens. Fu et al. [23] describes a new\nworkﬂow for converting natural language commands into Bash commands. The\nauthor uses ChatGPT to generate a candidate list of Bash commands based\non user input, and then uses a combination of heuristic and machine learning\ntechniques to rank and select the most likely candidates. This workﬂow was\nevaluated on a real command dataset and achieved high accuracy compared","page_label":"10","subject":""}


created_at: 2026-06-01 06:01:51
topic: persistent://default/default/3bd21dcf-6467-4133-ba61-7146653b53cb
id: 612e6914-8ce1-47b8-a245-bb5891416958
encoding: FLOAT32
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{"author":"","trapped":"/False","chroma:document":"evaluated on a real command dataset and achieved high accuracy compared\nto other state-of-the-art methods. Chen et al. [24] used the Bart model and\nChatGPT for the task of summarizing humorous titles and compared the perfor-\nmance of the two models. It was found that the Bart model performed better on\nlarge datasets, but ChatGPT was competitive with our best ﬁne-tuned model\nin a small range (48), albeit slightly weaker.\nThe following article uses ChatGPT to generate sentences.Chen et al. [25]\nconstructed a dialogue dataset (HPD) with scenes, timelines, character at-\ntributes, and character relationships in order to use ChatGPT as a conver-\nsational agent to generate dialogue. However, ChatGPT’s performance on the\ntest set was poor, and there is room for improvement.In study [26], chatGPT\ndemonstrated its ability to simplify complex text by providing three ﬁctional\nradiology reports to chatGPT for simpliﬁcation. Most radiologists found the","page":9,"creator":"LaTeX with hyperref","producer":"pdfTeX-1.40.21","page_label":"10","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","title":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","keywords":"","moddate":"2023-04-05T00:33:07+00:00","creationdate":"2023-04-05T00:33:07+00:00","subject":"","total_pages":35}


created_at: 2026-06-01 06:01:51
topic: persistent://default/default/3bd21dcf-6467-4133-ba61-7146653b53cb
id: da3ef5e9-b670-4170-95ee-0facbd748ffd
encoding: FLOAT32
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{"page_label":"10","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","total_pages":35,"chroma:document":"radiology reports to chatGPT for simpliﬁcation. Most radiologists found the\nsimpliﬁed reports to be accurate and complete, with no potential harm to pa-\ntients. However, some errors, omissions of critical medical information and text\npassages were identiﬁed, which could potentially lead to harmful conclusions if\nnot understood by the physicians. Xia et al. [27] proposes a new program re-\npair paradigm called Session-based Automatic Program Repair (APR). In APR,\nthe previously generated patches are iteratively built upon by combining them\n10","creationdate":"2023-04-05T00:33:07+00:00","keywords":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","page":9,"title":"","subject":"","moddate":"2023-04-05T00:33:07+00:00","author":"","producer":"pdfTeX-1.40.21","creator":"LaTeX with hyperref","trapped":"/False"}


created_at: 2026-06-01 06:01:51
topic: persistent://default/default/3bd21dcf-6467-4133-ba61-7146653b53cb
id: ad8cdad3-d00b-4c86-87d4-0219be0ddc9e
encoding: FLOAT32
metadata:
{"total_pages":35,"page":10,"page_label":"11","moddate":"2023-04-05T00:33:07+00:00","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","producer":"pdfTeX-1.40.21","subject":"","creator":"LaTeX with hyperref","trapped":"/False","title":"","creationdate":"2023-04-05T00:33:07+00:00","chroma:document":"with validation feedback to construct the model’s input. The eﬀectiveness of\nthe approach is veriﬁed using the QuixBugs dataset. The experiment shows\nthat ChatGPT ﬁne-tuned with reinforcement learning from human feedback\n(RLHF) outperforms Codex trained unsupervisedly in both repair datasets. In\nreference to study [28], ChatGPT was compared to three commercial transla-\ntion products: Google Translate2, DeepL Translate3, and Tencent TranSmart4.\nThe evaluation was conducted on the Flores101 test set, using the WMT19\nbiomedical translation task to test translation robustness, with BLEU score as\nthe main metric. The study found that ChatGPT is competitive with commer-\ncial translation products on high-resource European languages but falls behind\non low-resource or distant languages. The authors explored an interesting strat-\negy called pivot prompts, which signiﬁcantly improved translation performance.\nWhile ChatGPT did not perform as well as commercial systems on biomedical","author":"","keywords":""}


created_at: 2026-06-01 06:01:51
topic: persistent://default/default/3bd21dcf-6467-4133-ba61-7146653b53cb
id: 7742ebbd-668f-422b-89bf-93be809ae7f8
encoding: FLOAT32
metadata:
{"source":"./data/Summary of ChatGPTGPT-4 Research.pdf","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","keywords":"","total_pages":35,"creator":"LaTeX with hyperref","subject":"","page_label":"11","moddate":"2023-04-05T00:33:07+00:00","creationdate":"2023-04-05T00:33:07+00:00","producer":"pdfTeX-1.40.21","chroma:document":"While ChatGPT did not perform as well as commercial systems on biomedical\nabstracts or Reddit comments, it may be a good speech translator. Prieto et\nal. [29] evaluated the use of ChatGPT in developing an automated construction\nschedule based on natural language prompts. The experiment required building\nnew partitions in an existing space and providing details on the rooms to be\npartitioned. The results showed that ChatGPT was able to generate a coher-\nent schedule that followed a logical approach to meet the requirements of the\ngiven scope. However, there were still several major ﬂaws that would limit the\nuse of this tool in real-world projects.Michail et al. [30] proposed a method\nto improve the prediction accuracy of the HeFit ﬁne-tuned XLM T model on\ntweet intimacy by generating a dataset of tweets with intimacy rating tags using\nChatGPT. The speciﬁc operation is to input tweets with intimacy rating tags\ninto ChatGPT and then output similar tweets.","author":"","page":10,"title":"","trapped":"/False"}


created_at: 2026-06-01 06:01:51
topic: persistent://default/default/3bd21dcf-6467-4133-ba61-7146653b53cb
id: 8817edc5-173c-4070-b01d-2f91c8da0b4c
encoding: FLOAT32
metadata:
{"page":10,"author":"","total_pages":35,"trapped":"/False","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","moddate":"2023-04-05T00:33:07+00:00","chroma:document":"into ChatGPT and then output similar tweets.\nThe following article uses ChatGPT to generate paragraphs. Wang et al.\n[31] compared the abstract summarization performance of ChatGPT and other\nmodels on various cross-lingual text datasets and found that ChatGPT may\nperform worse in metrics such as R 1, R 2, R L, and B S. Yang et al. [32]\nsummarized the performance of ChatGPT in question answering-based text\nsummarization and found that, compared to ﬁne-tuned models, ChatGPT’s\nperformance is slightly worse in all performance metrics. However, the article\nsuggests that if the dataset is golden annotation, ChatGPT’s performance may\nsurpass ﬁne-tuned models in these metrics. Belouadi et al. [33] compared the\nability of ByGPT5 and ChatGPT trained on a range of labeled and unlabeled\ndatasets of English and German poetry to generate constrained style poetry, and\nevaluated them using three metrics: Rhyme, ScoreAlliteration, and ScoreMe-","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","keywords":"","creationdate":"2023-04-05T00:33:07+00:00","subject":"","producer":"pdfTeX-1.40.21","title":"","creator":"LaTeX with hyperref","page_label":"11"}


created_at: 2026-06-01 06:01:51
topic: persistent://default/default/3bd21dcf-6467-4133-ba61-7146653b53cb
id: 7b7d5fc3-9ee4-4765-8821-af85a5bbeaec
encoding: FLOAT32
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{"trapped":"/False","chroma:document":"evaluated them using three metrics: Rhyme, ScoreAlliteration, and ScoreMe-\nter Score. The conclusion is that ByGPT5 performs better than ChatGPT.\nBlanco-Gonzalez et al. [34] evaluated chatGPT’s ability to write commentary\narticles, and in fact, this article itself was written by chatGPT. The human au-\nthor rewrote the manuscript based on chatGPT’s draft. Experts found that it\ncan quickly generate and optimize text, as well as help users complete multiple\ntasks. However, in terms of generating new content, it is not ideal. Ultimately,\nit can be said that without strong human intervention, chatGPT is not a use-\nful tool for writing reliable scientiﬁc texts. It lacks the knowledge and expertise\nrequired to accurately and fully convey complex scientiﬁc concepts and informa-\n11","keywords":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","total_pages":35,"moddate":"2023-04-05T00:33:07+00:00","author":"","title":"","creationdate":"2023-04-05T00:33:07+00:00","creator":"LaTeX with hyperref","page":10,"page_label":"11","producer":"pdfTeX-1.40.21","subject":""}


created_at: 2026-06-01 06:01:51
topic: persistent://default/default/3bd21dcf-6467-4133-ba61-7146653b53cb
id: 523b255f-e825-488c-9d55-8e031cff9da1
encoding: FLOAT32
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{"producer":"pdfTeX-1.40.21","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","page_label":"12","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","trapped":"/False","subject":"","keywords":"","chroma:document":"tion. Khalil et al. [35] on the originality of content generated by ChatGPT. To\nevaluate the originality of 50 papers on various topics generated by ChatGPT,\ntwo popular plagiarism detection tools, Turnitin and iThenticate, were used.\nThe results showed that ChatGPT has great potential in generating complex\ntext output that is not easily captured by plagiarism detection software. The\nexisting plagiarism detection software should update their plagiarism detection\nengines. Basic et al. [36] conducted a comparison of the writing performance\nof students using or not using ChatGPT-3 as a writing aid. The experiment\nconsisted of two groups of 9 participants each. The control group wrote articles\nusing traditional methods, while the experimental group used ChatGPT as an\naid. Two teachers evaluated the papers. The study showed that the assistance of\nChatGPT did not necessarily improve the quality of the students’ essays.Noever","creator":"LaTeX with hyperref","author":"","title":"","total_pages":35,"page":11,"creationdate":"2023-04-05T00:33:07+00:00","moddate":"2023-04-05T00:33:07+00:00"}


created_at: 2026-06-01 06:01:51
topic: persistent://default/default/3bd21dcf-6467-4133-ba61-7146653b53cb
id: 43bb1179-c36c-4e92-a4d8-5bd197018ffc
encoding: FLOAT32
metadata:
{"total_pages":35,"creationdate":"2023-04-05T00:33:07+00:00","producer":"pdfTeX-1.40.21","title":"","moddate":"2023-04-05T00:33:07+00:00","creator":"LaTeX with hyperref","subject":"","page_label":"12","page":11,"chroma:document":"ChatGPT did not necessarily improve the quality of the students’ essays.Noever\net al. [37] discusses the potential of using artiﬁcial intelligence (AI), particularly\nlanguage models like GPT (including GPT-3), to create more convincing chat-\nbots that can deceive humans into thinking they are interacting with another\nperson. The article describes a series of experiments in which they used GPT-3\nto generate chatbot responses that mimic human-like conversations and were\ntested on human participants. The results show that some participants were\nunable to distinguish between the chatbot and a real human, highlighting the\npotential for these AI chatbots to be used for deceptive purposes.\n2.1.4 Code Generation\nCode generation refers to the process of automatically generating computer code\nfrom high-level descriptions or speciﬁcations. ChatGPT’s advanced natural lan-\nguage processing capabilities make it capable of performing code generation","keywords":"","author":"","trapped":"/False","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","source":"./data/Summary of ChatGPTGPT-4 Research.pdf"}


created_at: 2026-06-01 06:01:51
topic: persistent://default/default/3bd21dcf-6467-4133-ba61-7146653b53cb
id: 70c7db94-4e94-466e-a58b-917f641e378f
encoding: FLOAT32
metadata:
{"page_label":"12","moddate":"2023-04-05T00:33:07+00:00","total_pages":35,"chroma:document":"guage processing capabilities make it capable of performing code generation\ntasks. By analyzing the requirements for code generation, ChatGPT can pro-\nduce code snippets that accurately execute the intended functionality. This not\nonly saves time and eﬀort in writing code from scratch but also reduces the risk\nof errors that may occur during manual coding. In addition, ChatGPT’s ability\nto learn and adapt to new programming languages and frameworks enables it\nto complete more complex programming tasks. For example:\nMegahed et al. [38] discussed the potential of using ChatGPT for tasks\nsuch as code explanation, suggesting alternative methods for problem-solving\nwith code, and translating code between programming languages. The solutions\nprovided by ChatGPT were found to be viable. In another study, Treude et\nal. [39] introduced a ChatGPT-based prototype called GPTCOMCARE, which\nhelps programmers generate multiple solutions for a programming problem and","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","producer":"pdfTeX-1.40.21","creationdate":"2023-04-05T00:33:07+00:00","subject":"","page":11,"creator":"LaTeX with hyperref","trapped":"/False","title":"","keywords":"","author":""}


created_at: 2026-06-01 06:01:51
topic: persistent://default/default/3bd21dcf-6467-4133-ba61-7146653b53cb
id: 8a52b8c0-f2ec-44d6-a573-bcb8495f4b19
encoding: FLOAT32
metadata:
{"subject":"","creator":"LaTeX with hyperref","trapped":"/False","creationdate":"2023-04-05T00:33:07+00:00","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","page_label":"12","title":"","keywords":"","author":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","total_pages":35,"producer":"pdfTeX-1.40.21","moddate":"2023-04-05T00:33:07+00:00","page":11,"chroma:document":"helps programmers generate multiple solutions for a programming problem and\nhighlight the diﬀerences between each solution using colors.Sobania et al. [40]\nutilized ChatGPT for code bug ﬁxing, and further improved the success rate of\nbug ﬁxing by inputting more information through its dialogue system. Speciﬁ-\ncally, the QuixBugs standard bug ﬁxing benchmark contained 40 code bugs that\nneeded to be ﬁxed. With limited information, ChatGPT ﬁxed 19 bugs, which\nwas slightly lower than the 21 bugs ﬁxed by the Codex model, but signiﬁcantly\nhigher than the 7 ﬁxed by the Standard APR model. When given more prompts\nand information, ChatGPT was able to ﬁx 31 bugs, demonstrating its potential\n12"}


created_at: 2026-06-01 06:01:51
topic: persistent://default/default/3bd21dcf-6467-4133-ba61-7146653b53cb
id: 59acdd6f-0e8e-4f2a-9d9f-7a913d7859a1
encoding: FLOAT32
metadata:
{"page_label":"13","author":"","trapped":"/False","title":"","subject":"","producer":"pdfTeX-1.40.21","page":12,"moddate":"2023-04-05T00:33:07+00:00","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","creator":"LaTeX with hyperref","keywords":"","total_pages":35,"chroma:document":"for code bug ﬁxing tasks.Xia et al. [27] proposed a conversational approach\nfor Automated Program Repair (APR), which alternates between generating\npatches and validating them against feedback from test cases until the correct\npatch is generated. Selecting 30 bugs from the QuixBugs standard bug ﬁxing\nbenchmark, which are suitable for test case feedback, and demonstrating them\nwith Java and Python, the QuixBugs-Python and QuixBugs-Java datasets were\nobtained. The conversational APR using ChatGPT outperformed the conversa-\ntional APR using Codex and the conversational APR using CODEGEN (with\nmodel parameters of 350M, 2B, 6B, and 16B) on both datasets. Furthermore,\nChatGPT’s conversational APR generated and validated patches with signiﬁ-\ncantly fewer feedback loops than the other models.\nChatGPT can not only be used to achieve some simple code generation tasks\nbut also can be used to accomplish some complex programming tasks. Noever","creationdate":"2023-04-05T00:33:07+00:00"}


created_at: 2026-06-01 06:01:51
topic: persistent://default/default/3bd21dcf-6467-4133-ba61-7146653b53cb
id: 4817acc6-a6d3-419b-87ea-92561753e76d
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{"trapped":"/False","moddate":"2023-04-05T00:33:07+00:00","keywords":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","chroma:document":"but also can be used to accomplish some complex programming tasks. Noever\net al. [41] tested ChatGPT’s code generation capabilities on four datasets -\nIris, Titanic, Boston Housing, and Faker. When prompted to mimic a Python\ninterpreter in the form of a Jupyter notebook, the model was able to generate\nindependent code based on the prompt and respond with the expected out-\nput. For example, when given the prompt ”data.cor()” for the Iris dataset,\nChatGPT generated correct Python output. The test results indicate that\nChatGPT can access structured datasets and perform basic software opera-\ntions required by databases, such as create, read, update, and delete (CRUD).\nThis suggests that cutting-edge language models like ChatGPT have the nec-\nessary scale to tackle complex problems. McKee et al. [42] utilized ChatGPT\nas an experimental platform to investigate cybersecurity issues. They modeled\nﬁve diﬀerent modes of computer virus properties, including self-replication, self-","creationdate":"2023-04-05T00:33:07+00:00","author":"","total_pages":35,"subject":"","producer":"pdfTeX-1.40.21","page":12,"page_label":"13","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","creator":"LaTeX with hyperref","title":""}


created_at: 2026-06-01 06:01:51
topic: persistent://default/default/3bd21dcf-6467-4133-ba61-7146653b53cb
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{"total_pages":35,"title":"","creator":"LaTeX with hyperref","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","trapped":"/False","creationdate":"2023-04-05T00:33:07+00:00","chroma:document":"ﬁve diﬀerent modes of computer virus properties, including self-replication, self-\nmodiﬁcation, execution, evasion, and application, using ChatGPT. These ﬁve\nmodes encompassed thirteen encoding tasks from credential access to defense\nevasion within the MITRE ATT&CK framework. The results showed that the\nquality of ChatGPT’s generated code was generally above average, except for the\nself-replication mode, where it performed poorly.They [43] also employed Chat-\nGPT as a network honeypot to defend against attackers. By having ChatGPT\nmimic Linux, Mac, and Windows terminal commands and providing interfaces\nfor TeamViewer, nmap, and ping, a dynamic environment can be created to\nadapt to attackers’ operations, and logs can be used to gain insight into their\nattack methods, tactics, and procedures. The authors demonstrated ten hon-\neypot tasks to illustrate that ChatGPT’s interface not only provides suﬃcient\nAPI memory to execute previous commands without defaulting to repetitive","author":"","producer":"pdfTeX-1.40.21","moddate":"2023-04-05T00:33:07+00:00","page":12,"page_label":"13","keywords":"","subject":""}


created_at: 2026-06-01 06:01:51
topic: persistent://default/default/3bd21dcf-6467-4133-ba61-7146653b53cb
id: 52623f65-78c7-4b61-9df1-edc8ecba250c
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{"keywords":"","total_pages":35,"moddate":"2023-04-05T00:33:07+00:00","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","page":12,"page_label":"13","author":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","creationdate":"2023-04-05T00:33:07+00:00","trapped":"/False","chroma:document":"API memory to execute previous commands without defaulting to repetitive\nintroductory tasks but also oﬀers a responsive welcome program that maintains\nattackers’ interest in multiple queries.\nIn the ﬁeld of code generation, there are still several challenges with Chat-\nGPT. Firstly, its application scope is limited as its training data is biased to-\nwards programming languages such as Python, C++, and Java, making it po-\ntentially unsuitable for some programming languages or coding styles. Secondly,\nmanual optimization is necessary for code formatting, as the generated code may\nnot be performance-optimized or follow best coding practices, requiring manual\nediting and optimization. Lastly, the quality of the generated code cannot be\n13","creator":"LaTeX with hyperref","title":"","producer":"pdfTeX-1.40.21","subject":""}


created_at: 2026-06-01 06:01:51
topic: persistent://default/default/3bd21dcf-6467-4133-ba61-7146653b53cb
id: c4b42ddb-17fe-4300-b4fd-cb91be3bdb64
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{"trapped":"/False","page_label":"14","moddate":"2023-04-05T00:33:07+00:00","creationdate":"2023-04-05T00:33:07+00:00","total_pages":35,"ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","page":13,"source":"./data/Summary of ChatGPTGPT-4 Research.pdf","title":"","subject":"","creator":"LaTeX with hyperref","producer":"pdfTeX-1.40.21","author":"","keywords":"","chroma:document":"guaranteed, as it heavily relies on the quality of the natural language input,\nwhich may contain errors, ambiguities, or inconsistencies, ultimately aﬀecting\nthe accuracy and reliability of the generated code.\n2.1.5 Inference\nInference refers to the process of drawing new conclusions or information through\nlogical deduction from known facts or information. It is typically based on a se-\nries of premises or assumptions, and involves applying logical rules or reasoning\nmethods to arrive at a conclusion. Inference is an important ability in human\nthinking, and is often used to solve problems, make decisions, analyze and eval-\nuate information, etc. Inference also plays a key role in ﬁelds such as science,\nphilosophy, law, etc. There are two types of inference: inductive reasoning,\nwhich involves deriving general rules or conclusions from known facts or expe-\nriences, and deductive reasoning, which involves deriving speciﬁc conclusions"}


created_at: 2026-06-01 06:01:51
topic: persistent://default/default/3bd21dcf-6467-4133-ba61-7146653b53cb
id: 916dd505-6998-43e7-ab49-f1e9bd97396a
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{"producer":"pdfTeX-1.40.21","creationdate":"2023-04-05T00:33:07+00:00","subject":"","page_label":"14","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","chroma:document":"riences, and deductive reasoning, which involves deriving speciﬁc conclusions\nfrom known premises or assumptions. Whether inductive or deductive, the pro-\ncess of inference requires following strict logical rules to ensure the correctness\nand reliability of the inference.\nSome papers attempt to use ChatGPT’s ability in inductive reasoning to\ncapture the meaning in text and use deﬁned metrics to score the text. Michail\net al. [30] uses ChatGPT to infer intimacy expressed in tweets. They ﬁrst input\n50 tweets with intimacy markers to ChatGPT, then use inductive reasoning to\ninfer the standards for generating tweets with diﬀerent levels of intimacy, and\nﬁnally generate ten tweets with intimacy values ranging from 0 to 5. Susnjak\net al. [44] collected a large amount of textual data from patient-doctor dis-\ncussion forums, patient testimonials, social media platforms, medical journals,\nand other scientiﬁc research publications. Using the BERT model, the author","total_pages":35,"page":13,"source":"./data/Summary of ChatGPTGPT-4 Research.pdf","trapped":"/False","title":"","creator":"LaTeX with hyperref","author":"","keywords":"","moddate":"2023-04-05T00:33:07+00:00"}


created_at: 2026-06-01 06:01:51
topic: persistent://default/default/3bd21dcf-6467-4133-ba61-7146653b53cb
id: d120d8de-ffd2-415e-ba99-3a5aaaa6bf50
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{"source":"./data/Summary of ChatGPTGPT-4 Research.pdf","page":13,"creator":"LaTeX with hyperref","total_pages":35,"subject":"","moddate":"2023-04-05T00:33:07+00:00","keywords":"","author":"","creationdate":"2023-04-05T00:33:07+00:00","chroma:document":"and other scientiﬁc research publications. Using the BERT model, the author\ninferred emotion values from 0 to 1. The author visualized the process of how\nthe presence of bias in the discourse surrounding chronic manifestations of the\ndisease using the SHAP tool. The author also envisioned ChatGPT as a replace-\nment for the BERT model for scoring the emotional value of text. Huang et al.\n[21] chose 12.5% of individuals in the potential hate dataset as study materials,\ninduced ChatGPT to make classiﬁcations based on a prompt, and ChatGPT\nproduced three classiﬁcations: unclear, yes, and no. The author assigned a\nvalue of 1 to yes, -1 to no, and 0 to unclear, and had ChatGPT score and clas-\nsify them. ChatGPT was able to correctly classify 80% of implicit hate tweets\nin the author’s experimental setup, demonstrating ChatGPT’s great potential\nas a data labeling tool using simple prompts.\nSome papers have evaluated ChatGPT’s reasoning performance, mainly in","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","page_label":"14","producer":"pdfTeX-1.40.21","trapped":"/False","title":""}


created_at: 2026-06-01 06:01:51
topic: persistent://default/default/3bd21dcf-6467-4133-ba61-7146653b53cb
id: 2a80bac1-51a9-4151-ac20-3e7f06888a74
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{"creationdate":"2023-04-05T00:33:07+00:00","author":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","keywords":"","creator":"LaTeX with hyperref","producer":"pdfTeX-1.40.21","subject":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","page":13,"trapped":"/False","chroma:document":"Some papers have evaluated ChatGPT’s reasoning performance, mainly in\ndecision-making and spatial reasoning, and identifying ambiguity. Tang et al.\n[45] used the independence axiom and the transitivity axiom, as well as other\nnon-VNM related decision-making abilities, by presenting bets conditioned on\nrandom events, bets with asymmetric outcomes, decisions encapsulating Sav-\nage’s Sure Thing principle, and other complex bet structures like nested bets, to\ndesign experiments where each experiment input a short prompt to ChatGPT\nand evaluated the results. The conclusion is that ChatGPT exhibits uncer-\n14","page_label":"14","total_pages":35,"title":"","moddate":"2023-04-05T00:33:07+00:00"}


created_at: 2026-06-01 06:01:51
topic: persistent://default/default/3bd21dcf-6467-4133-ba61-7146653b53cb
id: 56382a33-bd7a-4231-aa0f-0ec6091af9de
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{"source":"./data/Summary of ChatGPTGPT-4 Research.pdf","total_pages":35,"title":"","creator":"LaTeX with hyperref","subject":"","author":"","producer":"pdfTeX-1.40.21","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","trapped":"/False","page":14,"page_label":"15","keywords":"","creationdate":"2023-04-05T00:33:07+00:00","moddate":"2023-04-05T00:33:07+00:00","chroma:document":"tainty in the decision-making process: in some cases, large language models\ncan arrive at the correct answer through incorrect reasoning; and it may make\nsuboptimal decisions for simple reasoning problems. Ortega-Martn et al. [46]\nhad ChatGPT detect three diﬀerent levels of language ambiguity and evaluated\nits performance. The conclusion is that In semantics, ChatGPT performed per-\nfectly in the detection of ambiguities. Apart from that, it has some bright sports\n(co-reference resolution) and some weaknesses (puts gender bias over grammar\nin some non-ambiguous situations). In the generation task ChatGPT did well,\nbut also revealed some of its worse issues: the lack of systematicity. Lastly, it\nshould also be pointed that in most of the cases ChatGPT brilliantly alludes to\nlack of context as the key factor in disambiguation.\n2.1.6 Data or information extraction, transformation, enhancement,\nprocessing\nData Visualization"}


created_at: 2026-06-01 06:01:51
topic: persistent://default/default/3bd21dcf-6467-4133-ba61-7146653b53cb
id: a8f54423-9dc1-4e9b-8378-f6587b701bf9
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{"creator":"LaTeX with hyperref","creationdate":"2023-04-05T00:33:07+00:00","total_pages":35,"author":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","trapped":"/False","keywords":"","subject":"","page_label":"15","title":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","page":14,"chroma:document":"2.1.6 Data or information extraction, transformation, enhancement,\nprocessing\nData Visualization\nNatural language interfaces have contributed to generating visualizations\ndirectly from natural language, but visualization problems remain challenging\ndue to the ambiguity of natural language.ChatGPT provides a new avenue for\nthe ﬁeld by converting natural language into visualized code.\nIn terms of data visualization, Noever et al. [41] tested ChatGPT’s ba-\nsic arithmetic skills by asking questions.On the iris dataset, Titanic survival\ndataset, Boston housing data, and randomly generated insurance claims dataset,\nthe statistical analysis of data and visualization problems were converted to\nprogramming problems using Jupyter to verify ChatGPT’s ability to generate\npython code to draw suitable graphs and analyze the data. The results show\nthat ChatGPT can access structured and organized datasets to perform the\nfour basic software operations required for databases: create, read, update, and","producer":"pdfTeX-1.40.21","moddate":"2023-04-05T00:33:07+00:00"}


created_at: 2026-06-01 06:01:51
topic: persistent://default/default/3bd21dcf-6467-4133-ba61-7146653b53cb
id: 33d1d376-9f33-456f-8762-05ccbf302b09
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{"moddate":"2023-04-05T00:33:07+00:00","producer":"pdfTeX-1.40.21","chroma:document":"four basic software operations required for databases: create, read, update, and\ndelete, and generate suitable python code to plot graphs for descriptive statis-\ntics, variable correlation analysis, describing trends, and other data analysis\noperations.Maddigan et al. [47] proposed an end-to-end solution for visualizing\ndata in natural language using LLM, which uses an open-source python frame-\nwork designed to generate appropriate hints for selected datasets to make LLM\nmore eﬀective in understanding natural language, and uses internal reasoning\ncapabilities to select the appropriate visualization type to generate the code for\nvisualization. In this paper,the reseachers compare the visualization results of\nGPT-3, Codex and ChatGPT in the case of nvBench SQLite database [48] and\nthe visualization results of energy production dataset in the study of ADVISor\nwith NL4DV [49, 50].In addition to, they explore the ability to reason and hy-","author":"","title":"","creationdate":"2023-04-05T00:33:07+00:00","creator":"LaTeX with hyperref","total_pages":35,"ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","subject":"","trapped":"/False","page_label":"15","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","page":14,"keywords":""}


created_at: 2026-06-01 06:01:51
topic: persistent://default/default/3bd21dcf-6467-4133-ba61-7146653b53cb
id: 9bf45a24-91b0-4c9e-9f16-605080183455
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{"subject":"","title":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","creationdate":"2023-04-05T00:33:07+00:00","author":"","chroma:document":"with NL4DV [49, 50].In addition to, they explore the ability to reason and hy-\npothesize of the LLM on movie dataset [48] when the hints are insuﬃcient or\nwrong .Experimental results show that LLM can eﬀectively support the end-to-\nend generation of visualization results from natural language when supported\nby hints, providing an eﬃcient, reliable and accurate solution to the natural\nlanguage visualization problem.\n15","keywords":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","creator":"LaTeX with hyperref","total_pages":35,"trapped":"/False","producer":"pdfTeX-1.40.21","page_label":"15","moddate":"2023-04-05T00:33:07+00:00","page":14}


created_at: 2026-06-01 06:01:51
topic: persistent://default/default/3bd21dcf-6467-4133-ba61-7146653b53cb
id: fd5006ef-cdb3-417a-9eb9-423958215ba1
encoding: FLOAT32
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{"moddate":"2023-04-05T00:33:07+00:00","chroma:document":"Information Extraction\nThe goal of information extraction is to extract speciﬁc information from\nnatural language text for structured representation, including three important\nsubtasks such as entity relationship extraction, named entity recognition, and\nevent extraction, which have wide applications in business, medical, and other\nﬁelds.\nIn information extraction, Wei et al. [51] proposed ChatIE, a ChatGPT-\nbased multi-round question-and-answer framework for information extraction.\nThe framework decomposes a complex information extraction (IE) task into\nseveral parts, then combines the results of each round into a ﬁnal structured\nresult. The entity association triple extraction, named entity recognition, and\nevent extraction tasks were performed on six datasets NYT11-HRL, DuIE2.0\n, conllpp, MSR , DuEE1.0 [52, 53, 54, 55, 56], and ACE05 in both languages,\ncomparing three metrics of precision, recall, and F1 score.These results sug-","title":"","author":"","keywords":"","page":15,"total_pages":35,"trapped":"/False","page_label":"16","subject":"","creationdate":"2023-04-05T00:33:07+00:00","producer":"pdfTeX-1.40.21","creator":"LaTeX with hyperref","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","source":"./data/Summary of ChatGPTGPT-4 Research.pdf"}


created_at: 2026-06-01 06:01:51
topic: persistent://default/default/3bd21dcf-6467-4133-ba61-7146653b53cb
id: 797feb7c-ce4d-429f-aa16-21c71ad1aeb5
encoding: FLOAT32
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{"creationdate":"2023-04-05T00:33:07+00:00","page":15,"author":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","chroma:document":"comparing three metrics of precision, recall, and F1 score.These results sug-\ngest that on six widely used IE datasets, ChatIE improves performance by an\naverage of 18.98% compared to the original ChatGPT without ChatIE, and out-\nperforms the supervised models FCM and MultiR [57, 58] on the NYT11-HRL\ndataset.While the original ChatGPT cannot solve complex IE problems with\noriginal task instructions, and with this framework, successfully IE tasks were\nimplemented on six datasets.Gao et al. [59] explored the feasibility and chal-\nlenges of ChatGPT for event extraction on the ACE2005 corpus, evaluating the\nperformance of ChatGPT in long-tail and complex scenarios (texts containing\nmultiple events) and comparing it with two task-speciﬁc models, Text2Event\nand EEQA [60, 61].Then,they explored the impact of diﬀerent cues on perfor-\nmance of ChatGPT. The results show that the average performance of Chat-\nGPT in long-tail and complex scenarios is only 51.04% of that of task-speciﬁc","trapped":"/False","title":"","keywords":"","producer":"pdfTeX-1.40.21","page_label":"16","subject":"","total_pages":35,"creator":"LaTeX with hyperref","moddate":"2023-04-05T00:33:07+00:00","source":"./data/Summary of ChatGPTGPT-4 Research.pdf"}


created_at: 2026-06-01 06:01:51
topic: persistent://default/default/3bd21dcf-6467-4133-ba61-7146653b53cb
id: 51922ba3-db16-461e-90c1-cd8b7c5085ab
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{"ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","producer":"pdfTeX-1.40.21","title":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","page_label":"16","subject":"","author":"","creationdate":"2023-04-05T00:33:07+00:00","moddate":"2023-04-05T00:33:07+00:00","creator":"LaTeX with hyperref","chroma:document":"GPT in long-tail and complex scenarios is only 51.04% of that of task-speciﬁc\nmodels such as EEQA. Continuous reﬁnement of cues does not lead to consis-\ntent performance improvements, and ChatGPT is highly sensitive to diﬀerent\ncue styles.Tang et al. [62] proposed a new training paradigm that incorporates\nappropriate cues to guide ChatGPT to generate a variety of examples with dif-\nferent sentence structures and language patterns and eliminate the resulting\nlow-quality or duplicate samples for downstream tasks. Although compared to\na soft model for a speciﬁc healthcare tasks, ChatGPT underperforms in Named\nEntity Recognition (NER) and Relationship Extraction (RE) tasks , in the Gene\nAssociation Database (GAD) Release; EU-ADR corpus for the RE task , the\ninnovative training framework was able to train local models, with F1 scores im-\nproving from 23.37% to 63.99% for the named entity recognition task and from","keywords":"","page":15,"total_pages":35,"trapped":"/False"}


created_at: 2026-06-01 06:01:51
topic: persistent://default/default/3bd21dcf-6467-4133-ba61-7146653b53cb
id: 677a57af-a09a-4524-9e28-d5a4255ffc1b
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{"producer":"pdfTeX-1.40.21","keywords":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","creationdate":"2023-04-05T00:33:07+00:00","title":"","trapped":"/False","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","total_pages":35,"page_label":"16","creator":"LaTeX with hyperref","chroma:document":"proving from 23.37% to 63.99% for the named entity recognition task and from\n75%, while alleviating privacy concerns and time-consuming data collection and\nannotation problems.He et al. [63] proposed a contextual learning framework\nICL- D3IE. this framework introduces formatted presentation, continuously it-\nerates to update and improve the presentation, and then combines ChatGPT\nfor text information extraction. In the paper, ICL-D3IE is compared with ex-\nisting pre-trained models such as LiLT,BROS (in-distribution (ID) setting and\nout-of-distribution (OOD) setting) on datasets (FUNSD, CORD, and SROIE\n[64, 65, 66]).These results show that the ICL-D3IE method in all datasets and\nsettings except for the ID setting on CORD are superior to other methods,\n16","subject":"","author":"","moddate":"2023-04-05T00:33:07+00:00","page":15}


created_at: 2026-06-01 06:01:51
topic: persistent://default/default/3bd21dcf-6467-4133-ba61-7146653b53cb
id: b4d7eb6a-e9ea-4c9a-a325-2d560e80626f
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{"trapped":"/False","subject":"","total_pages":35,"author":"","moddate":"2023-04-05T00:33:07+00:00","creationdate":"2023-04-05T00:33:07+00:00","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","keywords":"","page":16,"ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","chroma:document":"with ICL-D3IE (GPT-3) F1 scores reaching 90.32% on FUNSD and97.88% on\nSROIE; in the out-of-distribution (OOD) setting, ICL-D3IE performs much\nbetter than previous pre-trained methods on all datasets.Polak et al. [67] pro-\nposed ChatExtract method - consisting of a set of engineering prompts applied\nto a conversational LLM - for automatic data extraction. In the experiment,\nthey extracted a large number of sentences from hundreds of papers and ran-\ndomly selected 100 sentences containing data and 100 sentences without data\nas test data. The results show that the accuracy and recall of LLM exceeded\n90% and may be comparable to human accuracy in many cases; in addition to\nthis, the experiments were conducted under the condition of removing follow-up\nprompts and not keeping the conversation compared to previous experiments,\nrespectively. The accuracy of deleting follow-up questions dropped to 80.2%\nand the recall rate dropped to 88.0%. Removing the conversational aspect and","producer":"pdfTeX-1.40.21","page_label":"17","title":"","creator":"LaTeX with hyperref"}


created_at: 2026-06-01 06:01:51
topic: persistent://default/default/3bd21dcf-6467-4133-ba61-7146653b53cb
id: 1e8c2c7c-4a46-4619-aa2e-bd4c1b61cf60
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{"creationdate":"2023-04-05T00:33:07+00:00","page_label":"17","chroma:document":"and the recall rate dropped to 88.0%. Removing the conversational aspect and\nrelated information retention recall and accuracy dropped to 90.0% and 56.6%,\nrespectively, demonstrating the eﬀect of information retention combined with\npurposeful redundancy on LLM information extraction performance.\nQuality Assessment\nFor translation quality, text generation quality, manual assessment is usually\neﬀective but suﬀers from subjectivity and time-consuming, etc. It was found\nthrough exploration that ChatGPT has also achieved signiﬁcant performance\nin automatic quality assessment.\nIn terms of quality assessment,Kocmi et al. [68] proposed a GPT-based\ntranslation quality assessment metric, GEMBA, which evaluates the transla-\ntion of each fragment individually and then averages all the obtained scores to\nobtain a ﬁnal system-level score. In the MQM2022 test set (English-German,\nEnglish-Russian, and Chinese-English) [69], a scoring task was performed with","total_pages":35,"producer":"pdfTeX-1.40.21","keywords":"","creator":"LaTeX with hyperref","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","moddate":"2023-04-05T00:33:07+00:00","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","trapped":"/False","subject":"","title":"","page":16,"author":""}


created_at: 2026-06-01 06:01:51
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{"page_label":"17","creator":"LaTeX with hyperref","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","title":"","moddate":"2023-04-05T00:33:07+00:00","total_pages":35,"subject":"","keywords":"","creationdate":"2023-04-05T00:33:07+00:00","producer":"pdfTeX-1.40.21","page":16,"trapped":"/False","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","author":"","chroma:document":"English-Russian, and Chinese-English) [69], a scoring task was performed with\na classiﬁcation task to compare the accuracy [70] and kendall tau scores [71] of\nseven GPT models under four cue templates.The results showed that GEMBA\nhad the highest system-level accuracy of 88.0% compared to more than 10 au-\ntomatic metrics such as BLEU, and among the seven GPT models, ChatGPT\naccuracy is above 80%, in addition to, the best performance can be obtained in\nthe least constrained template, demonstrating the potential of LLM for trans-\nlation quality assessment tasks, but the evaluation is only applicable at the\nsystem level and needs further improvement.Wang et al. [72] used ChatGPT\nas a natural language generation (NLG) evaluator to study the correlation with\nhuman judgment. On three datasets covering diﬀerent NLG tasks, task- and\naspect-speciﬁc cues were designed to guide ChatGPT for NLG evaluation in\nCNN/DM [73], OpenMEVA-ROC, and BAGEL for summary, story generation,"}


created_at: 2026-06-01 06:01:51
topic: persistent://default/default/3bd21dcf-6467-4133-ba61-7146653b53cb
id: 4ff9b0aa-0f6e-4b4d-b23c-ca70a485ca60
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{"keywords":"","title":"","creationdate":"2023-04-05T00:33:07+00:00","chroma:document":"CNN/DM [73], OpenMEVA-ROC, and BAGEL for summary, story generation,\nand data-to-text scoring, respectively. Then,they compute Spearman coeﬃ-\ncients [74],Pearson correlation coeﬃcients [75]. Kendall’s Tau score [76] to as-\nsess the correlation with human evaluations.The results show that ChatGPT is\nhighly correlated with human judgments in all aspects, with correlation coeﬃ-\ncients of 0.4 or more in all categories, showing its potential as an NLG indicator.\n17","total_pages":35,"moddate":"2023-04-05T00:33:07+00:00","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","author":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","page":16,"trapped":"/False","subject":"","creator":"LaTeX with hyperref","page_label":"17","producer":"pdfTeX-1.40.21"}


created_at: 2026-06-01 06:01:51
topic: persistent://default/default/3bd21dcf-6467-4133-ba61-7146653b53cb
id: 83fc4e2e-17c8-4544-96db-f2beeadd84ea
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{"creator":"LaTeX with hyperref","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","total_pages":35,"page_label":"18","moddate":"2023-04-05T00:33:07+00:00","producer":"pdfTeX-1.40.21","trapped":"/False","keywords":"","creationdate":"2023-04-05T00:33:07+00:00","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","title":"","author":"","subject":"","page":17,"chroma:document":"Data Augmentation\nIn natural language processing, text data augmentation is an eﬀective mea-\nsure to alleviate the problem of low data quantity and low quality training data,\nand ChatGPT has shown great potential in this regard.\nIn terms of data augmentation, Dai et al. [77] proposed a ChatGPT-based\ntext data augmentation method that reformulates each sentence in the train-\ning sample into multiple conceptually similar but semantically diﬀerent samples\nfor classiﬁcation tasks downstream of the Bert model.On text transcriptions\nand PubMed 20k datasets containing more than 8 hours of audio data of com-\nmon medical symptom descriptions,experiments were conducted to compare co-\nsine similarity and TransRate metrics with multiple data enhancement methods\n[9].This paper shows that compared with existing data enhancement methods,\nthe proposed ChatAug method shows a double-digit improvement in sentence\nclassiﬁcation accuracy and generates more diverse augmented samples while"}


created_at: 2026-06-01 06:01:51
topic: persistent://default/default/3bd21dcf-6467-4133-ba61-7146653b53cb
id: 556b4ce3-8db3-4e8d-9b93-d519b6bb9984
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{"author":"","moddate":"2023-04-05T00:33:07+00:00","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","page":17,"creationdate":"2023-04-05T00:33:07+00:00","subject":"","trapped":"/False","total_pages":35,"page_label":"18","title":"","chroma:document":"classiﬁcation accuracy and generates more diverse augmented samples while\nmaintaining its accuracy, but the original model is not ﬁne-tuned in the pa-\nper and suﬀers from a lack of domain knowledge, which may produce incorrect\naugmented data.\nMultimodal fusion\nChatGPT can currently only process natural language directly, but with a\ncross-modal encoder, it can combine natural language with cross-modal pro-\ncessing to provide solutions for intelligent transportation, healthcare, and other\nﬁelds.\nIn terms of multimodal data processing, Wu et al. [78] constructed a frame-\nwork that Visual ChatGPT integrates with diﬀerent Visual Foundation Models\n(VFMs) and then combines a series of hints to input visual information to Chat-\nGPT to solve visual problems.The paper shows examples of visual tasks such\nas removing or replacing certain objects from images, interconversion between\nimages and text, demonstrating the Visual ChatGPT has great potential and","keywords":"","creator":"LaTeX with hyperref","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","producer":"pdfTeX-1.40.21"}


created_at: 2026-06-01 06:01:51
topic: persistent://default/default/3bd21dcf-6467-4133-ba61-7146653b53cb
id: ca3d51c2-a052-400d-a67f-abfc2535dee4
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{"page_label":"18","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","trapped":"/False","total_pages":35,"keywords":"","subject":"","creationdate":"2023-04-05T00:33:07+00:00","title":"","producer":"pdfTeX-1.40.21","author":"","moddate":"2023-04-05T00:33:07+00:00","chroma:document":"images and text, demonstrating the Visual ChatGPT has great potential and\ncapability for diﬀerent tasks.But there are issues during the task that require\na large number of hints to convert VFMs to language, invoke multiple VFMs\nto solve complex problems leading to limited real-time capability, and security\nand privacy issues. Zheng et al. [79] showed a text mining example of LLM\nfor extracting self-driving car crash data from California crash news, analyz-\ning a failure report example, and generating a crash report example based on\nkeywords; introduced a use case concept of a smartphone-based framework for\nautomatic LLM failure report generation, which absorbs multiple data sources\ncaptured by cell phone sensors and then transfers the data to a language space\nfor text mining, inference and generation, and further outputs the key informa-\ntion needed to form a comprehensive fault report, demonstrating the potential\nof LLM for a variety of transportation tasks.","page":17,"ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","creator":"LaTeX with hyperref"}


created_at: 2026-06-01 06:01:51
topic: persistent://default/default/3bd21dcf-6467-4133-ba61-7146653b53cb
id: cd744db9-761d-41b0-a6a8-4d5c96622cbe
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{"creationdate":"2023-04-05T00:33:07+00:00","chroma:document":"of LLM for a variety of transportation tasks.\nNowadays, ChatGPT shows a wide range of applications in data visualiza-\ntion, information extraction, data enhancement, quality assessment, and multi-\nmodal data processing.But there are also issues on how to further utilize hints\nto eﬀectively interact with ChatGPT, lack of ability to process and analyze data\n18","page_label":"18","keywords":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","creator":"LaTeX with hyperref","subject":"","title":"","author":"","page":17,"trapped":"/False","moddate":"2023-04-05T00:33:07+00:00","total_pages":35,"producer":"pdfTeX-1.40.21","source":"./data/Summary of ChatGPTGPT-4 Research.pdf"}


created_at: 2026-06-01 06:01:51
topic: persistent://default/default/3bd21dcf-6467-4133-ba61-7146653b53cb
id: 90416de3-c887-44a2-9620-631015c66fb1
encoding: FLOAT32
metadata:
{"source":"./data/Summary of ChatGPTGPT-4 Research.pdf","page_label":"19","moddate":"2023-04-05T00:33:07+00:00","author":"","creator":"LaTeX with hyperref","title":"","producer":"pdfTeX-1.40.21","chroma:document":"from devices such as sensors, and data privacy and security.\nCueing Techniques\nCue engineering provides important support for eﬀective dialogue with large\nlanguage models.White et al. [80] proposed a framework for cueing models\napplicable to diﬀerent domains. This framework structures cues to interact\nwith LLMs by providing speciﬁc rules and guidelines. Also, this paper presents\na catalog of cueing patterns that have been applied to LLM interactions, as well\nas speciﬁc examples with and without cues. The advantages of the combinability\nof prompting patterns are demonstrated, allowing users to interact with LLM\nmore eﬀectively, but patterns for reusable solutions and new ways to use LLM\nneed to be continuously explored.\n2.1.7 Human-ChatGPT Collaboration\nCollaboration between humans and machines is a process where humans and\nmachines work together to achieve a common goal. In such collaboration, hu-\nmans provide domain expertise, creativity, and decision-making abilities, while","creationdate":"2023-04-05T00:33:07+00:00","page":18,"keywords":"","subject":"","trapped":"/False","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","total_pages":35}


created_at: 2026-06-01 06:01:51
topic: persistent://default/default/3bd21dcf-6467-4133-ba61-7146653b53cb
id: 16ccdcb5-527c-437b-a18a-36080ab24df4
encoding: FLOAT32
metadata:
{"total_pages":35,"title":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","page_label":"19","producer":"pdfTeX-1.40.21","keywords":"","creationdate":"2023-04-05T00:33:07+00:00","chroma:document":"mans provide domain expertise, creativity, and decision-making abilities, while\nmachines provide automation, scalability, and computing power. ChatGPT is\nan advanced natural language processing model that can understand and gen-\nerate human-like language, thereby reducing communication costs. Its ability\nto process and generate natural language makes it an ideal partner for human\ncollaboration. ChatGPT can oﬀer relevant suggestions, complete tasks based\non human input, and enhance human productivity and creativity. It can learn\nfrom human feedback and adapt to new tasks and domains, further improv-\ning its performance in human-machine collaboration. ChatGPT’s capability to\ncomprehend natural language and produce appropriate responses makes it a\nvaluable tool for various collaboration applications, as demonstrated by several\nstudies in the literature we have gathered.\nAhmad et al. [81] proposed a method for human-machine collaboration us-","creator":"LaTeX with hyperref","subject":"","moddate":"2023-04-05T00:33:07+00:00","trapped":"/False","page":18,"author":""}


created_at: 2026-06-01 06:01:51
topic: persistent://default/default/3bd21dcf-6467-4133-ba61-7146653b53cb
id: 6e154c0a-3b7c-4721-8982-a83cc3bf6ede
encoding: FLOAT32
metadata:
{"source":"./data/Summary of ChatGPTGPT-4 Research.pdf","subject":"","page_label":"19","title":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","keywords":"","trapped":"/False","total_pages":35,"creationdate":"2023-04-05T00:33:07+00:00","author":"","creator":"LaTeX with hyperref","producer":"pdfTeX-1.40.21","page":18,"moddate":"2023-04-05T00:33:07+00:00","chroma:document":"Ahmad et al. [81] proposed a method for human-machine collaboration us-\ning ChatGPT to create software architecture. This method transforms software\nstories (created by software architects based on application scenarios) into feasi-\nble software architecture diagrams through continuous interaction between the\nsoftware architect and ChatGPT. During the evaluation stage, ChatGPT uses\nthe Software Architecture Analysis Method (SAAM) to evaluate each compo-\nnent in the software architecture and generate evaluation reports. This method\neﬃciently utilizes the knowledge and supervision of the architect with the ca-\npabilities of ChatGPT to collaboratively build software-intensive systems and\nservices. Lanzi et al. [82] proposed a collaborative design framework that com-\nbines interactive evolution and ChatGPT to simulate typical human design pro-\ncesses. Humans collaborate with large language models (such as ChatGPT) to"}


created_at: 2026-06-01 06:01:51
topic: persistent://default/default/3bd21dcf-6467-4133-ba61-7146653b53cb
id: 069fe03e-9a61-4c6e-81c9-ea2b91d73258
encoding: FLOAT32
metadata:
{"title":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","keywords":"","subject":"","author":"","trapped":"/False","page_label":"19","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","producer":"pdfTeX-1.40.21","chroma:document":"cesses. Humans collaborate with large language models (such as ChatGPT) to\nrecombine and transform ideas, and use genetic algorithms to iterate through\ncomplex creative tasks. The results of three game design tasks showed that the\nframework received positive feedback from game designers. The framework has\ngood reusability and can be applied to any design task that can be described in\nfree text form.\n19","page":18,"moddate":"2023-04-05T00:33:07+00:00","creator":"LaTeX with hyperref","creationdate":"2023-04-05T00:33:07+00:00","total_pages":35}


created_at: 2026-06-01 06:01:51
topic: persistent://default/default/3bd21dcf-6467-4133-ba61-7146653b53cb
id: ce99cbab-cf95-4148-9c7b-74142cf7debf
encoding: FLOAT32
metadata:
{"page_label":"20","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","keywords":"","author":"","title":"","total_pages":35,"trapped":"/False","page":19,"source":"./data/Summary of ChatGPTGPT-4 Research.pdf","creator":"LaTeX with hyperref","subject":"","creationdate":"2023-04-05T00:33:07+00:00","producer":"pdfTeX-1.40.21","moddate":"2023-04-05T00:33:07+00:00","chroma:document":"In the future, ChatGPT’s ability to understand nonverbal cues such as tone\nof voice and body language can be enhanced, enabling it to better understand\nhuman thoughts and interact with people more eﬀectively.\n2.1.8 ChatGPT Integration\nIntegration refers to combining diﬀerent systems or software components to\nachieve a common goal. ChatGPT can be integrated as a part of a whole or\nact as an integration tool to enable seamless communication between diﬀerent\nsystems. Its natural language processing ability makes it easier for non-technical\nusers to interact with systems, reducing the need for specialized knowledge or\ntraining. Some studies in the literature we collected have already demonstrated\nthis.\nTreude et al. [39] integrated ChatGPT into the prototype of ”GPTCOM-\nCARE” to address programming query problems. This integration allowed for\nthe generation of multiple source code solutions for the same query, which in-"}


created_at: 2026-06-01 06:01:51
topic: persistent://default/default/3bd21dcf-6467-4133-ba61-7146653b53cb
id: cb329b51-5125-44e1-b4c0-b404a7c80d04
encoding: FLOAT32
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{"producer":"pdfTeX-1.40.21","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","trapped":"/False","chroma:document":"the generation of multiple source code solutions for the same query, which in-\ncreased the eﬃciency of software development. The results of their study demon-\nstrated the eﬀectiveness of using ChatGPT to improve the quality and diversity\nof code solutions, ultimately reducing the amount of time and eﬀort required for\nsoftware development.Wang et al. [83] proposed the chatCAD method, which\nutilizes large language models (LLMs) such as ChatGPT to enhance the out-\nput of multiple CAD networks for medical images, including diagnosis, lesion\nsegmentation, and report generation networks. The method generates sugges-\ntions in the form of a chat dialogue. The authors tested the eﬀectiveness of\nthe method on a randomly selected set of 300 cases from the MIMIC-CXR\ndataset, which included 50 cases each of cardiomegaly, edema, consolidation,\natelectasis, pleural eﬀusion, and no ﬁndings. Compared to CvT2DistilGPT2\nand R2GenCMN, chatCAD showed signiﬁcant advantages in RC and F1, while","page":19,"creator":"LaTeX with hyperref","subject":"","page_label":"20","keywords":"","creationdate":"2023-04-05T00:33:07+00:00","title":"","author":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","moddate":"2023-04-05T00:33:07+00:00","total_pages":35}


