# embeddings_queue

created_at: 2026-05-29 09:24:38
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: 26ce75f8-7ad7-4ba0-bcf2-90a6a948db49
encoding: FLOAT32
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{"page":24,"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,"producer":"pdfTeX-1.40.21","creator":"LaTeX with hyperref","subject":"","author":"","keywords":"","moddate":"2023-04-05T00:33:07+00:00","title":"","trapped":"/False","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","page_label":"25","creationdate":"2023-04-05T00:33:07+00:00"}


created_at: 2026-05-29 09:24:38
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: cda7c150-0c6d-44dd-9cd2-be37b5e2bc3f
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{"title":"","page_label":"26","creationdate":"2023-04-05T00:33:07+00:00","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.","author":"","creator":"LaTeX with hyperref","moddate":"2023-04-05T00:33:07+00:00","keywords":"","trapped":"/False","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","total_pages":35,"source":"./data/Summary of ChatGPTGPT-4 Research.pdf","page":25}


created_at: 2026-05-29 09:24:38
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: b7878d34-4ac7-44a5-8a57-d243aba119b3
encoding: FLOAT32
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{"page":25,"moddate":"2023-04-05T00:33:07+00:00","title":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","subject":"","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","total_pages":35,"page_label":"26","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","trapped":"/False","keywords":"","author":""}


created_at: 2026-05-29 09:24:38
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: 31697920-1e67-41db-826b-51af7e09d931
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","moddate":"2023-04-05T00:33:07+00:00","author":"","page":25,"creator":"LaTeX with hyperref","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","producer":"pdfTeX-1.40.21","subject":"","title":"","total_pages":35,"page_label":"26","trapped":"/False","creationdate":"2023-04-05T00:33:07+00:00","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-"}


created_at: 2026-05-29 09:24:38
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: e5a7d814-dcbe-478d-a59d-4315dbcd98a7
encoding: FLOAT32
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{"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","author":"","page_label":"26","trapped":"/False","keywords":"","creator":"LaTeX with hyperref","subject":"","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","total_pages":35,"title":"","producer":"pdfTeX-1.40.21","creationdate":"2023-04-05T00:33:07+00:00","page":25}


created_at: 2026-05-29 09:24:38
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: cb2ae75e-d7f5-42f7-b4d1-2f67eeb1cbc3
encoding: FLOAT32
metadata:
{"page":26,"producer":"pdfTeX-1.40.21","moddate":"2023-04-05T00:33:07+00:00","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","author":"","creationdate":"2023-04-05T00:33:07+00:00","total_pages":35,"subject":"","keywords":"","creator":"LaTeX with hyperref","page_label":"27","title":"","trapped":"/False","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","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."}


created_at: 2026-05-29 09:24:38
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: 76b280a0-7e7e-48f8-897b-0fc4b692f1b4
encoding: FLOAT32
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{"page_label":"27","moddate":"2023-04-05T00:33:07+00:00","total_pages":35,"author":"","page":26,"creationdate":"2023-04-05T00:33:07+00:00","subject":"","trapped":"/False","keywords":"","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","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","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-"}


created_at: 2026-05-29 09:24:38
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: a68efc67-3e6c-45a8-9817-05ad1af3880b
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","page_label":"27","title":"","creator":"LaTeX with hyperref","chroma:document":"[7] Frieder S, Pinchetti L, Griﬃths RR, Salvatori T, Lukasiewicz T, Pe-\ntersen PC, et al. Mathematical capabilities of chatgpt. arXiv preprint\narXiv:230113867. 2023.\n[8] Pardos ZA, Bhandari S. Learning gain diﬀerences between ChatGPT and\nhuman tutor generated algebra hints. arXiv preprint arXiv:230206871.\n2023.\n[9] Shakarian P, Koyyalamudi A, Ngu N, Mareedu L. An Independent Eval-\nuation of ChatGPT on Mathematical Word Problems (MWP). arXiv\npreprint arXiv:230213814. 2023.\n27","producer":"pdfTeX-1.40.21","keywords":"","trapped":"/False","author":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","page":26,"creationdate":"2023-04-05T00:33:07+00:00","total_pages":35,"moddate":"2023-04-05T00:33:07+00:00","subject":""}


created_at: 2026-05-29 09:24:38
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: 757e4aed-16ff-4862-a4a3-14b704e425a9
encoding: FLOAT32
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{"keywords":"","author":"","page":27,"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","creator":"LaTeX with hyperref","producer":"pdfTeX-1.40.21","subject":"","total_pages":35,"title":"","page_label":"28","moddate":"2023-04-05T00:33:07+00:00","creationdate":"2023-04-05T00:33:07+00:00","chroma:document":"[10] Lehnert K. AI Insights into Theoretical Physics and the Swampland Pro-\ngram: A Journey Through the Cosmos with ChatGPT. arXiv preprint\narXiv:230108155. 2023.\n[11] Kortemeyer G. Could an Artiﬁcial-Intelligence agent pass an introductory\nphysics course? arXiv preprint arXiv:230112127. 2023.\n[12] West CG. AI and the FCI: Can ChatGPT Project an Understanding of\nIntroductory Physics? arXiv preprint arXiv:230301067. 2023.\n[13] Nov O, Singh N, Mann DM. Putting ChatGPT’s Medical Advice to the\n(Turing) Test. medRxiv. 2023.\n[14] Tu R, Ma C, Zhang C. Causal-Discovery Performance of ChatGPT in the\ncontext of Neuropathic Pain Diagnosis. arXiv preprint arXiv:230113819.\n2023.\n[15] Glymour C, Zhang K, Spirtes P. Review of Causal Discovery Methods\nBased on Graphical Models. Frontiers in Genetics. 2019.\n[16] Guo S, Wang Y, Li S, Saeed N. Semantic Communications with Ordered\nImportance using ChatGPT. arXiv preprint arXiv:230207142. 2023."}


created_at: 2026-05-29 09:24:38
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: d5284b67-6389-4c95-b9db-a507da880dd0
encoding: FLOAT32
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{"page":27,"moddate":"2023-04-05T00:33:07+00:00","author":"","keywords":"","creator":"LaTeX with hyperref","subject":"","total_pages":35,"producer":"pdfTeX-1.40.21","trapped":"/False","creationdate":"2023-04-05T00:33:07+00:00","page_label":"28","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","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-","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","title":""}


created_at: 2026-05-29 09:24:38
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: e17d24b8-716e-46f2-9a51-2b5f093830c8
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","total_pages":35,"subject":"","trapped":"/False","producer":"pdfTeX-1.40.21","creator":"LaTeX with hyperref","creationdate":"2023-04-05T00:33:07+00:00","moddate":"2023-04-05T00:33:07+00:00","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","page":27,"author":"","title":"","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","keywords":"","page_label":"28"}


created_at: 2026-05-29 09:24:38
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: acec0034-94d6-4b78-a163-18df659d8782
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","keywords":"","author":"","total_pages":35,"trapped":"/False","subject":"","producer":"pdfTeX-1.40.21","creationdate":"2023-04-05T00:33:07+00:00","page_label":"29","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","moddate":"2023-04-05T00:33:07+00:00","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.","creator":"LaTeX with hyperref","title":"","page":28}


created_at: 2026-05-29 09:24:38
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: 25831336-7667-4206-bcd5-0cea6f3d5ec9
encoding: FLOAT32
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{"subject":"","author":"","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","page_label":"29","page":28,"creationdate":"2023-04-05T00:33:07+00:00","producer":"pdfTeX-1.40.21","chroma:document":"2023 mar;13(4):857. Available from: https://doi.org/10.3390%\n2Fbuildings13040857.\n[30] Michail A, Konstantinou S, Clematide S. UZH CLyp at SemEval-2023\nTask 9: Head-First Fine-Tuning and ChatGPT Data Generation for\nCross-Lingual Learning in Tweet Intimacy Prediction. arXiv preprint\narXiv:230301194. 2023.\n[31] Wang J, Liang Y, Meng F, Li Z, Qu J, Zhou J. Cross-Lingual Summa-\nrization via ChatGPT. arXiv preprint arXiv:230214229. 2023.\n[32] Yang X, Li Y, Zhang X, Chen H, Cheng W. Exploring the limits of\nchatgpt for query or aspect-based text summarization. arXiv preprint\narXiv:230208081. 2023.\n[33] Belouadi J, Eger S. ByGPT5: End-to-End Style-conditioned Po-\netry Generation with Token-free Language Models. arXiv preprint\narXiv:221210474. 2022.\n[34] Blanco-Gonzalez A, Cabezon A, Seco-Gonzalez A, Conde-Torres D,\nAntelo-Riveiro P, Pineiro A, et al. The Role of AI in Drug Discovery: Chal-\nlenges, Opportunities, and Strategies. arXiv preprint arXiv:221208104.\n2022.","keywords":"","moddate":"2023-04-05T00:33:07+00:00","creator":"LaTeX with hyperref","title":"","total_pages":35,"trapped":"/False"}


created_at: 2026-05-29 09:24:38
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: bc6556af-5b46-44a7-9ca0-78110831a57a
encoding: FLOAT32
metadata:
{"title":"","subject":"","keywords":"","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","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","creator":"LaTeX with hyperref","producer":"pdfTeX-1.40.21","page":28,"author":"","creationdate":"2023-04-05T00:33:07+00:00","page_label":"29","total_pages":35,"moddate":"2023-04-05T00:33:07+00:00"}


created_at: 2026-05-29 09:24:38
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: dde670e2-14cb-4ecd-bf1d-198dc09e9d02
encoding: FLOAT32
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{"source":"./data/Summary of ChatGPTGPT-4 Research.pdf","subject":"","creator":"LaTeX with hyperref","page_label":"30","keywords":"","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","page":29,"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":"","producer":"pdfTeX-1.40.21","moddate":"2023-04-05T00:33:07+00:00","title":"","total_pages":35,"trapped":"/False"}


created_at: 2026-05-29 09:24:38
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: b71d4ce5-e5a0-4aea-8d43-9be4c7550dd2
encoding: FLOAT32
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{"moddate":"2023-04-05T00:33:07+00:00","page":29,"author":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","total_pages":35,"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","creator":"LaTeX with hyperref","page_label":"30","producer":"pdfTeX-1.40.21","keywords":"","title":"","subject":"","chroma:document":"arXiv:230103771. 2023.\n[44] Susnjak T. Applying BERT and ChatGPT for Sentiment Analysis of\nLyme Disease in Scientiﬁc Literature. arXiv preprint arXiv:230206474.\n2023.\n[45] Tang Z, Kejriwal M. A Pilot Evaluation of ChatGPT and DALL-E 2 on\nDecision Making and Spatial Reasoning. arXiv preprint arXiv:230209068.\n2023.\n[46] Ortega-Mart´ ın M, Garc´ ıa-Sierra´O, Ardoiz A, ´Alvarez J, Armenteros JC,\nAlonso A. Linguistic ambiguity analysis in ChatGPT. arXiv preprint\narXiv:230206426. 2023.\n[47] Maddigan P, Susnjak T. Chat2vis: Generating data visualisations via\nnatural language using chatgpt, codex and gpt-3 large language models.\narXiv preprint arXiv:230202094. 2023.\n[48] Luo Y, Tang J, Li G. nvBench: A Large-Scale Synthesized Dataset for\nCross-Domain Natural Language to Visualization Task. arXiv preprint\narXiv:211212926. 2021.\n[49] Liu C, Han Y, Jiang R, Yuan X. Advisor: Automatic visualization answer\nfor natural-language question on tabular data. In: 2021 IEEE 14th Paciﬁc"}


created_at: 2026-05-29 09:24:38
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: 00f5665e-883d-4fce-9eaf-d5d4ccb0d89d
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{"total_pages":23,"source":"./data/서울대 미대.pdf","page":2,"page_label":"3","creationdate":"2024-02-26T13:44:15+09:00","creator":"Adobe InDesign 19.2 (Windows)","chroma:document":"4 5\n디자인전공소개\n디자인학부는 1998년 공예과와 산업디자인과를 통합하여 디자인학부로 개편하면서, \n‘21세기의 창의적 디자인 인재양성’과 ‘신 디자인가치 창조의 세계적 요람’을 교육의 \n목표로 정립하였다. 또한 이를 달성하기 위한 실천전략으로 문화중심 교육과 다학제적 \n교육, 개념창조 중심의 교육, 세계와의 협동 교육의 방향을 수립하였다. \n또한 대학원은 1996년 개교 50주년을 맞아 서울대학교가 수립한 장기발전구상에 \n따라, 21세기 지식기반사회를 이끌어갈 ‘고급 디자인지도자의 양성’과 ‘세계적 수준의 \n디자인연구 대학원’을 교육목표로 수립하고 이를 실천하기 위하여 21세기 문화연구와 \n디자인산업을 선도하는 교육, 세계 디자인에 기여하는 실천 전략들을 수립하여 \n진행하고 있다.\n1. 전공소개 및 학위명칭\n1-1\n서울대학교 미술대학 디자인과 디자인전공\n미술학사(디자인)\nBachelor of Fine Arts in Design\n전공분리 전\n전공분리 후","trapped":"/False","producer":"Adobe PDF Library 17.0","moddate":"2024-02-26T13:44:25+09:00"}


created_at: 2026-05-29 09:26:56
topic: persistent://default/default/fb85bf48-961e-4953-9ab3-7fbd469f8358
id: 5f39c0d3-9ef8-44ce-a5f8-ebd930940a47
encoding: FLOAT32
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{"trapped":"/False","producer":"Adobe PDF Library 17.0","page_label":"3","total_pages":23,"page":2,"source":"./data/서울대 미대.pdf","moddate":"2024-02-26T13:44:25+09:00","creationdate":"2024-02-26T13:44:15+09:00","creator":"Adobe InDesign 19.2 (Windows)","chroma:document":"Bachelor of Fine Arts in Design\n전공분리 전\n전공분리 후\n서울대학교 미술대학 디자인과 \n서울대학교 미술대학 디자인과 산업디자인전공\n서울대학교 미술대학 디자인과 시각디자인전공\n소속 및 명칭\n1-2\n◦ 명칭\n◦ 학위의 \n   명칭\n◦ 소속\n인간의 생활환경은 많은 구성원들로 이루어져 있으며 구성원들마다 서로간의 영향을 \n주고받으며 한 사회를 만들어 나간다. 또한 인간, 도구, 공간, 문화 등은 서로가 조화를 \n이루면서 한 사회를 만들어간다. 산업디자인 전공은 우리를 둘러싸고 한 사회와 \n문화를 만드는 여러 도구, 수단, 공간과 현상을 대상으로 보다 삶의 가치를 높이며, 그 \n필요성에 대하여 파악하고 이를 보다 창조적이며 미학적으로 형상화하는 창조행위로 \n이루어진다. 이에 본 전공은 구조적, 기능적, 형태적 특성을 결정하는 여러 과정을 \n통하여 인간, 도구, 공간 간의 조화를 추구하며, 제품디자인분야, 공간디자인분야,"}


created_at: 2026-05-29 09:26:56
topic: persistent://default/default/fb85bf48-961e-4953-9ab3-7fbd469f8358
id: 7be19031-49eb-4c2e-aafb-a78825ea5414
encoding: FLOAT32
metadata:
{"moddate":"2024-02-26T13:44:25+09:00","page_label":"3","total_pages":23,"producer":"Adobe PDF Library 17.0","page":2,"trapped":"/False","source":"./data/서울대 미대.pdf","chroma:document":"통하여 인간, 도구, 공간 간의 조화를 추구하며, 제품디자인분야, 공간디자인분야, \n운송수단디자인분야 등에서 형상화, 시각화시키고 있다. 이에 디자이너로서 갖추어야 \n하는 창의력과 미적 능력을 함양할 수 있는 과정을 제공하여 다양한 분야에서 각자의 \n가능성을 발휘할 수 있도록 한다.\n시각디자인은 매스커뮤니케이션 시대의 다양한 정보와 인간의 의사와 사물의 이미지를 \n보다 정확하고 합리적인 방법으로 전달하기 위한 창조행위이다.\n그래픽디자인 분야는 정보를 시각화하여 전달하는 형식을 디자인하는 분야로서 \n기본적으로 광고, 포스터, 타이포그래피, 일러스트레이션, 집단의 표시 및 행사의 \n이미지 관리를 위한 여러 매체의 시각통일 전달기법 등을 주로 다루게 된다. 더 나아가 \n미디어디자인분야는 영상편집디자인, 애니매이션, 인터랙션, UX 디자인, 서비스디자인 \n등과 같은 21세기의 새로운 조형영역인 영상미디어분야와 관련된 교과목들을 제공하고 \n있다.\n◦ 산업디자인전공\n◦ 시각디자인전공","creator":"Adobe InDesign 19.2 (Windows)","creationdate":"2024-02-26T13:44:15+09:00"}


created_at: 2026-05-29 09:26:56
topic: persistent://default/default/fb85bf48-961e-4953-9ab3-7fbd469f8358
id: 0174a9b2-6041-4307-ba3c-784ebc65876e
encoding: FLOAT32
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{"producer":"Adobe PDF Library 17.0","page_label":"3","creationdate":"2024-02-26T13:44:15+09:00","creator":"Adobe InDesign 19.2 (Windows)","source":"./data/서울대 미대.pdf","total_pages":23,"moddate":"2024-02-26T13:44:25+09:00","trapped":"/False","chroma:document":"있다.\n◦ 산업디자인전공\n◦ 시각디자인전공\n세부전공 소개\n1-3","page":2}


created_at: 2026-05-29 09:26:56
topic: persistent://default/default/fb85bf48-961e-4953-9ab3-7fbd469f8358
id: 2c0738c0-c1d4-4401-93f4-11f7f6d3ee32
encoding: FLOAT32
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{"creationdate":"2024-02-26T13:44:15+09:00","source":"./data/서울대 미대.pdf","creator":"Adobe InDesign 19.2 (Windows)","page_label":"4","page":3,"trapped":"/False","total_pages":23,"chroma:document":"6 7\n시각디자인 전공\n2-1\n담당분야 | 미디어디자인\n세부전공 | 인터렉션디자인, UX디자인\n이메일 | jheune@snu.ac.kr\nURL | intermedia.or.kr\n윤주현\n담당분야 | 미디어디자인\n세부전공 | 뉴미디어디자인\n이메일 | suzung@snu.ac.kr\nURL | www.suzung.com\n김수정\n담당분야 | 그래픽디자인\n세부전공 | 그래픽, 타이포그래피\n이메일 | ks1@snu.ac.kr\n김경선\n담당분야 | 그래픽디자인\n세부전공 | 공공커뮤니케이션, 브랜딩\n이메일 | leejangsub@snu.ac.kr\n이장섭\n담당분야 | 그래픽디자인\n세부전공 | Computational Design\n이메일 | chamamoto@snu.ac.kr\nChris Hamamoto\n디자인전공 학과장\n담당분야 | 공간디자인\n세부전공 | 공간디자인\n이메일 | sm.ahn@snu.ac.kr\n안성모\n산업디자인 전공\n2-2\n담당분야 | 제품디자인\n세부전공 | User Interface","moddate":"2024-02-26T13:44:25+09:00","producer":"Adobe PDF Library 17.0"}


created_at: 2026-05-29 09:26:56
topic: persistent://default/default/fb85bf48-961e-4953-9ab3-7fbd469f8358
id: 2e2f46bd-546f-4b53-abca-250df3adf7d9
encoding: FLOAT32
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{"page":3,"source":"./data/서울대 미대.pdf","moddate":"2024-02-26T13:44:25+09:00","chroma:document":"산업디자인 전공\n2-2\n담당분야 | 제품디자인\n세부전공 | User Interface\n이메일 | parkym@snu.ac.kr\nURL | cafe.naver.com/parkdesignlab\n박영목\n미술대학 학장\n담당분야 | 제품디자인\n세부전공 | UX디자인, HCI디자인\n이메일 | jech@snu.ac.kr\nURL | www.hcidlab.com\n정의철\n담당분야 | 리빙디자인\n세부전공 | 가구, 생활제품디자인\n이메일 | jang7047@snu.ac.kr\n장성연\n담당분야 | 디자인역사문화이론\n세부전공 | 디자인 역사· 이론· 비평 \n이메일 | getto@snu.ac.kr\nURL | www.snu-dhc.com\n김민수\n2. 교수진 소개","total_pages":23,"page_label":"4","producer":"Adobe PDF Library 17.0","creationdate":"2024-02-26T13:44:15+09:00","trapped":"/False","creator":"Adobe InDesign 19.2 (Windows)"}


created_at: 2026-05-29 09:26:56
topic: persistent://default/default/fb85bf48-961e-4953-9ab3-7fbd469f8358
id: b063d0de-edd8-4be5-a472-521488e3ea21
encoding: FLOAT32
metadata:
{"moddate":"2024-02-26T13:44:25+09:00","producer":"Adobe PDF Library 17.0","page_label":"5","trapped":"/False","creator":"Adobe InDesign 19.2 (Windows)","creationdate":"2024-02-26T13:44:15+09:00","source":"./data/서울대 미대.pdf","chroma:document":"8 9\n학생들은 소정의 서류를 제출하고 등록함으로써 학생의 신분을 취득하게 된다. 따라서 \n제일 먼저 등록 절차를 밟는 것이 필요한데, 보통 1학기 등록은 2월말 경, 2학기 등록은 \n8월말 경에 실시한다. 기한 내에 마이스누 > 등록,장학 > 등록에서 납부고지서를 \n출력하여 납부하며, 1회 납부가 어려운 경우 분납을 통해서 4차례에 나누어 납부할 수 \n있다.\n2024년 현재 0학점 등록이 불가능하므로 1학점 이상 수강 신청 하여야 하며, 규정학기 \n초과자 (9학기 이상 등록자)의 경우 별도의 기준을 통해 학점에 비례한 등록금을 \n납부한다. 등록기간 중에 등록이나 휴학 허가를 받지 않으면 미등록 제적되며, 복적생, \n재입학생, 직전학기 휴학생은 등록 전에 복적, 재입학, 복학(귀)의 허가를 받은 후 \n등록해야 한다.\n◦ 등록\n◦ 휴학의 제한 및 유의사항\n학사과정의 휴학은 6학기를 초과할 수 없다. (질병휴학, 군휴학 불포함)","total_pages":23,"page":4}


created_at: 2026-05-29 09:26:56
topic: persistent://default/default/fb85bf48-961e-4953-9ab3-7fbd469f8358
id: 52f341f1-b6b8-4581-a514-3939ae6a83dd
encoding: FLOAT32
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{"chroma:document":"학사과정의 휴학은 6학기를 초과할 수 없다. (질병휴학, 군휴학 불포함)\n학기 중 휴학 한 학생(군입대 포함)의 성적은 그 교과목의 수강신청을 취소한 것으로 \n처리한다. 그러므로 본인이 취소하지 않더라도 자동적으로 취소된다.\n등록하기 전에 휴학원을 제출하여 허가를 받은 학생은 그 학기 납입금이 면제되고, \n등록 후 휴학 허가를 받은 학생은 복학할 때 등록금이 면제된다. 휴학기간은 재학연한에 \n산입하지 아니한다.\n등록, 휴학, 복학, 퇴학, 제적\n3-1\n학생이 부득이한 사유로 휴학하고자 할 때에는 마이스누 > 개인정보 > 휴복학신청 \n메뉴에서 신청한다.  휴학기간은 기본적으로 1학기이며, 1학기 넘게 휴학을 원할 \n경우에는 1학기 뒤에 다시 휴학 신청을 해야 한다.\n• 일반휴학 : 등록금 납부기간 중에 등록금을 내지 않고 하는 휴학\n• 학기 중 휴학 : 등록한 학생으로서 수업일수의 2/4 이전까지 휴학원을","page":4,"page_label":"5","creationdate":"2024-02-26T13:44:15+09:00","trapped":"/False","total_pages":23,"producer":"Adobe PDF Library 17.0","creator":"Adobe InDesign 19.2 (Windows)","moddate":"2024-02-26T13:44:25+09:00","source":"./data/서울대 미대.pdf"}


created_at: 2026-05-29 09:26:56
topic: persistent://default/default/fb85bf48-961e-4953-9ab3-7fbd469f8358
id: 8170371d-e760-4105-ac0e-d49c35e43e00
encoding: FLOAT32
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{"page_label":"5","chroma:document":"• 학기 중 휴학 : 등록한 학생으로서 수업일수의 2/4 이전까지 휴학원을  \n                          제출하는 경우 (등록 후 수업 도중 휴학),  \n                          이후에는 군휴학 또는 질병휴학만 가능 \n• 종강 후 휴학 : 군복무. 군 대체복무 휴학에 한함\n※ 산업기능요원 선발을 위해 휴학하는 경우 ① 지정 업체 확인서(병무청의  \n   지정업체 선정 통보 공문 사본) ② 재직증명서 ③ 산업기능요원 편입예정확인서 \n   (지정업체 발행)를 첨부하여 휴학을 신청. (산업기능요원 편입을 위해 휴학하는  \n   학생은 제출서류를 첨부하여 일반휴학을 하고 편입 후 1주일 이내에  \n   산업기능요원 복무기록표를 제출하여 군 휴학으로 전환하여야 함.)\n복학시에는 마이스누 > 개인정보 > 휴복학신청 메뉴에서 복학을 신청한다.\n◦ 휴학 절차\n◦ 복학 절차\n◦ 퇴학 및 제적","producer":"Adobe PDF Library 17.0","page":4,"total_pages":23,"creationdate":"2024-02-26T13:44:15+09:00","source":"./data/서울대 미대.pdf","trapped":"/False","creator":"Adobe InDesign 19.2 (Windows)","moddate":"2024-02-26T13:44:25+09:00"}


created_at: 2026-05-29 09:26:56
topic: persistent://default/default/fb85bf48-961e-4953-9ab3-7fbd469f8358
id: 1ac4f1a1-2034-4a3e-a52a-0f1269010a4f
encoding: FLOAT32
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{"chroma:document":"◦ 휴학 절차\n◦ 복학 절차\n◦ 퇴학 및 제적\n퇴학하고자 하는 학생은 보증인 연서로 그 사유를 기입하여 대학(원)장에게 제출하고 \n대학(원)장의 허가를 얻어 퇴학할 수 있다. 퇴학 및 제적생은 등록 전에 복적, 재입학, \n복학(귀)의 허가를 받은 후 등록할 수 있다. 제적 기준은 다음과 같다.\n1. 휴학기간이 초과된 경우에는 제적 된다. (병역복무을 위한 때에는 예외로 한다.)\n2. 재학연한이 초과된 경우에는 제적 된다. (재입학 불가)\n3. 등록기간 중 휴학 허가 없이 등록하지 않을 경우에는 제적된다.\n4. 학사경고 4회 누적 시 학사위원회의 심의를 거쳐 제적된다.\n   * 학사경고 : 평점평균 1.7 미달 / 3과목 이상 F / 6학점 이상 F\n3. 학사일반","page":4,"producer":"Adobe PDF Library 17.0","source":"./data/서울대 미대.pdf","creationdate":"2024-02-26T13:44:15+09:00","trapped":"/False","total_pages":23,"page_label":"5","moddate":"2024-02-26T13:44:25+09:00","creator":"Adobe InDesign 19.2 (Windows)"}


created_at: 2026-05-29 09:26:56
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{"chroma:document":"10 11\n• 장학금 신청 \n장학금 신청기간에 서울대학교 포털(마이스누)를 통해 교내장학금을 신청한다.\n(신청 완료한 학생만이 선정 대상).\n※ 가계 곤란 장학금을 신청할 경우, 건강 보험 납부확인서 (부모 및 본인)\n    부모 납부합계액이 10만원 이하일 경우에만 서류제출, 건강보험증 사본\n• 수여기준 \n전액면제와 등록금의 일정부분의 장학금이 있으며, 매 학기 배정 예산에 따라 수여하며 \n수해 학생 수 및 금액에는 변동이 있다. 신청자에 한하여 심사를 하며 성적우수, \n가계곤란을 기준으로 심사한다. 금액의 배정은 세부전공별, 학년별로 배정한다.\n• 장학금 수령\n장학금은 등록금 고지서상에 표기, 장학금이 제외된 금액으로 고지서가 발급된다.\n◦ 교내장학금\n장학,근로장학생\n3-2\n• 근로장학생 신청절차\n마이스누에서 신청 후 출력하여 학과사무실에 제출한다. 각 근무지별로 지원자 심사 후 \n근로장학생을 선발한다. \n• 근로장학생의 업무","source":"./data/서울대 미대.pdf","trapped":"/False","moddate":"2024-02-26T13:44:25+09:00","page":5,"total_pages":23,"creationdate":"2024-02-26T13:44:15+09:00","creator":"Adobe InDesign 19.2 (Windows)","producer":"Adobe PDF Library 17.0","page_label":"6"}


created_at: 2026-05-29 09:26:56
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{"source":"./data/서울대 미대.pdf","chroma:document":"근로장학생을 선발한다. \n• 근로장학생의 업무\n1.  자신의 신청 부서에 따라서 다르지만 디자인전공 학과사무실에서는  \n    보조업무 및 청소, 디자인, 정리 등을 담당한다.\n2.  자신의 근무시간에 정해진 장소에서 근무해야하며 혹시 근무가 어려울 경우  \n    미리 근무지 책임자에게 양해를 구하고 대신 근무를 할 인원을 책임지고  \n    보충해야 한다. \n3.  근로장학생은 근무한 시간만큼 시간당 수당을 계산하여 매달 장학금을  \n    지급받는다.\n◦ 근로장학생","page_label":"6","moddate":"2024-02-26T13:44:25+09:00","page":5,"total_pages":23,"trapped":"/False","producer":"Adobe PDF Library 17.0","creator":"Adobe InDesign 19.2 (Windows)","creationdate":"2024-02-26T13:44:15+09:00"}


created_at: 2026-05-29 09:26:56
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{"moddate":"2024-02-26T13:44:25+09:00","total_pages":23,"page":6,"producer":"Adobe PDF Library 17.0","chroma:document":"12 13\n국내 학점 교류 협정을 맺은 타 대학교에서 수학 후 학점을 인정받는 것이 가능하며, \n매 학기 마이스누 > 정보광장 > 게시판 > 학생공지사항에 공지된다. 공지사항 확인 후 \n첨부파일에서 지원자 격 및 교류가능 대학을 확인한다.\n• 수학기간 : 학사과정은 4개 학기, 석사과정과 박사과정은 2개 학기\n• 계절학기 :  제한 없음\n• 등록학기의 인정 : 수학기간 동안 본교에 소정의 등록금을 납입하여  \n                                등록을 필하여야 함.\n• 취득학점 : 학기당 취득학점(대부분 9학점)은 본교 취득학점을 포함  \n                     17학점 이내 계절학기(6학점)는 9학점 이내로 함. \n※ 수학기간 동안 취득학점은 계절학기 취득학점을 포함하여 소속대학의  \n   학위취득에 필요한 최저 소요학점의 2분의 1 이내로 함.\n• 문의 : 대외협력실 02-880-7487 \n◦ 국내 교환학생, 방문학생\n교환학생, 방문학생\n3-3","trapped":"/False","source":"./data/서울대 미대.pdf","creationdate":"2024-02-26T13:44:15+09:00","creator":"Adobe InDesign 19.2 (Windows)","page_label":"7"}


created_at: 2026-05-29 09:26:56
topic: persistent://default/default/fb85bf48-961e-4953-9ab3-7fbd469f8358
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created_at: 2026-05-29 09:26:56
topic: persistent://default/default/fb85bf48-961e-4953-9ab3-7fbd469f8358
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{"page":6,"trapped":"/False","chroma:document":"네덜란드 헤이그 왕립예술대학(네덜란드)\n상기 대학들은 현재 미술대학, 미술대학 디자인학부와 협정을 맺은 대학들이며, 이와 관련한 \n수학 신청 공지는 서울대학교 미술대학 홈페이지 게시판에 공지된다. 그 외 본부협정을 맺는 \n대학으로의 국외 교환학생 신청은 대외협력본부 및 마이스누의 학생공지사항에 공지된다.\n국외 교환학생의 경우, 귀국 후 과사무실에 성적표 및 수료확인서, 강의계획서, 수업시간표 \n등을 제출하여 18학점 이내에서 학점 인정을 받을 수 있다. 이 경우 모두 일반선택(일선)교과로 \n인정되며, 제출한 자료에 의하여 증명되지 않을 시에는 학점 인정이 불가하니, 되도록 타대 수학 \n중 수업과 관련된 다양한 증명자료를 모아두는 것이 학점 인정에 많은 도움이 된다. 학점 인정은 \n전적으로 서울대의 학점 기준으로 인정된다. \n이론 교과의 경우엔 1학점 당 15시간 이상 이수하여야 하며, 실기교과의 경우 1학점 당 30시간","creationdate":"2024-02-26T13:44:15+09:00","moddate":"2024-02-26T13:44:25+09:00","source":"./data/서울대 미대.pdf","creator":"Adobe InDesign 19.2 (Windows)","total_pages":23,"producer":"Adobe PDF Library 17.0","page_label":"7"}


created_at: 2026-05-29 09:26:56
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{"total_pages":23,"source":"./data/서울대 미대.pdf","creationdate":"2024-02-26T13:44:15+09:00","page_label":"7","chroma:document":"이상 이수하여야 한다. 최대 학점인정은 학기 당 18학점까지만 가능하며, 미술대학 국제교류(\n외국대학 수학) 학생이 이수한 과목은 1과목당 최대 6학점까지 인정1)한다. 성적 또한 서울대에서 \n사용하고 있는 (A,B,C,D,F)기준으로 인정되므로 학점 변환 테이블을 같이 제출하는 것이 좋다. \n학점 인정은 학사위원회를 거쳐야 하기에 6개월 이상 소요될 수도 있으므로, 미리 자료를 모아 \n과사무실의 교무담당 조교에게 제출한다.\n1) 2017년 11월 7일 개정\n◦ 국외 교환/방문 학생","moddate":"2024-02-26T13:44:25+09:00","creator":"Adobe InDesign 19.2 (Windows)","page":6,"producer":"Adobe PDF Library 17.0","trapped":"/False"}


created_at: 2026-05-29 09:26:56
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{"total_pages":23,"source":"./data/서울대 미대.pdf","creator":"Adobe InDesign 19.2 (Windows)","trapped":"/False","producer":"Adobe PDF Library 17.0","page_label":"8","moddate":"2024-02-26T13:44:25+09:00","creationdate":"2024-02-26T13:44:15+09:00","page":7,"chroma:document":"14 15\n출국 전 마이스누 학사정보 > 대외교류 > 국제교환학생 > 국제교환학생 프로그램 \n신청에서 관련 서류를 스캔하여 업로드(PDF파일) 후 수학신청\n• 신청시기 : 정규학기 수학은 수학 희망학기 3개월 전,  \n                     계절학기 수학은 개강 2개월 전 신청\n• 관련서류 : 지도교수 및 학과장 확인을 받은 수학신청원, 지도교수 추천서,  \n                     교과구분확인서 (다전공관련 교과목)\n• 서류제출 : 원본서류는 학과에 제출 및 스캔하여 포털에 직접 업로드\n• 수강예정과목 미정의 경우 : 부득이하게 수강예정과목을 작성할 수 없을  \n                                                경우에는 사유를 기재하여 확인 받은 수강신청원을  \n                                                업로드(PDF파일)하고, 추후 수강과목이 정해지면"}


created_at: 2026-05-29 09:26:56
topic: persistent://default/default/fb85bf48-961e-4953-9ab3-7fbd469f8358
id: 8730935c-67d1-4cd2-80c4-b8c6fc019e4b
encoding: FLOAT32
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{"moddate":"2024-02-26T13:44:25+09:00","page":7,"trapped":"/False","producer":"Adobe PDF Library 17.0","source":"./data/서울대 미대.pdf","page_label":"8","creationdate":"2024-02-26T13:44:15+09:00","total_pages":23,"creator":"Adobe InDesign 19.2 (Windows)","chroma:document":"포탈에서 국외수학변경신청을 해야함\n• 다전공강의 수강 : 다전공과 관련된 교과목은 포탈 국외수학허가신청 화면에서  \n                                교과구분확인신청서를 출력하여 다전공 학과사무실에서  \n                                승인을 받아야함.\n※ 국외수학허가와 관련된 공지는 미술대학홈페이지 게시판을 참고할 것. \n    관련 서류 및 자세한 신청 절차가 안내되어 있음. \n• 이수교과목은 전공관련 교과목을 원칙으로함\n• 부득이하게 일반선택으로 이수할 경우 지도교수의 추천서 또는 특별한 사유를  \n   기재한 학과장 확인서 제출 : 국외수학허가신청 또는 변경 신청시 제출\n• 교양교과목 이수 불가\n• 동일교과목 이수 불가\n◦ 신청절차\n◦ 교과목이수\n• 변경절차 : 수강과목이 추후에 정해지거나 변경되는 경우에는 포탈에 관련서류를"}


created_at: 2026-05-29 09:26:56
topic: persistent://default/default/fb85bf48-961e-4953-9ab3-7fbd469f8358
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{"creationdate":"2024-02-26T13:44:15+09:00","moddate":"2024-02-26T13:44:25+09:00","trapped":"/False","page_label":"8","source":"./data/서울대 미대.pdf","chroma:document":"• 변경절차 : 수강과목이 추후에 정해지거나 변경되는 경우에는 포탈에 관련서류를  \n                    스캔하여 업로드(PDF파일) 후 국외수학허가 변경 신청\n• 관련서류 : 지도교수 및 학과장 확인을 받은 수학계획변경 내역서,  \n                    교과구분확인서(다전공관련교과목)\n• 수학기간 종료 후 즉시 학점인정 신청서, 교과목 강의시간 설명서를 작성하여  \n   성적증명서 원본, 수업강의계획서 등 수업시간 및 수업주수를 확인할 수 있는  \n   공식서류를 첨부하여 행정실에서 우선 검토 받은 후 지도교수와 학과장 확인 후 제출\n• 학점계산 : 이론수업 15시간 1학점, 실기수업 30시간 1학점\n• 이미 이수한 교과목과 동일한 교과목의 학점은 인정하지 아니함\n✽서울대학교 학칙\n제50조(수업일수) ①학사과정 및 대학원과정의 수업일수는 매학년 30주 이상으로 한다.","page":7,"creator":"Adobe InDesign 19.2 (Windows)","total_pages":23,"producer":"Adobe PDF Library 17.0"}


created_at: 2026-05-29 09:26:56
topic: persistent://default/default/fb85bf48-961e-4953-9ab3-7fbd469f8358
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{"source":"./data/서울대 미대.pdf","page_label":"8","moddate":"2024-02-26T13:44:25+09:00","trapped":"/False","creator":"Adobe InDesign 19.2 (Windows)","producer":"Adobe PDF Library 17.0","total_pages":23,"creationdate":"2024-02-26T13:44:15+09:00","page":7,"chroma:document":"제50조(수업일수) ①학사과정 및 대학원과정의 수업일수는 매학년 30주 이상으로 한다.\n제74조(학점) ①교과목 이수의 단위는 학점으로 하고 1학기간 15시간 이상의 강의를 1\n학점으로 하며, 실험·실습·실기 및 체육은 1학기간 30시간 이상의 수업을 1학점으로 한다.\n◦ 변경신청절차\n◦ 학점인정 신청절차"}


created_at: 2026-05-29 09:26:56
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{"producer":"Adobe PDF Library 17.0","total_pages":23,"creationdate":"2024-02-26T13:44:15+09:00","page_label":"9","trapped":"/False","chroma:document":"16 17\n수강신청을 하고 졸업기준에 맞는 학점을 취득하기 위해서는 우선 전공구분 및 \n교과구분에 대한 이해를 명확히 하여야 한다. 교과구분은 수업의 성격을 의미하는 \n것이며, 교양, 전선, 전필, 일선의 네 가지로 구분된다.\n• 교양 : 교양 수업은 전공생뿐만 아니라 여타 다른 모든 학생들도 들을 수 있는  \n             난이도의 수업으로 말 그대로 교양 함양을 위한 수업이다. 졸업하기  \n             위해서는 대학 글쓰기, 대학영어 및 핵심 교양 포함 총 36학점 이상의  \n             교양 수업을 들어야 한다. 교양 수업은 절대로 교과구분 수정이  \n             불가하므로 수강 시 교양수업 여부를 확실하게 체크하여야 한다.  \n             디자인전공에서 개설하는 교양수업으로는 ‘창조와 디자인’, ‘디자인과  \n             생활’이 있으며, 이 수업들을 제외한 모든 디자인 전공의 수업은","source":"./data/서울대 미대.pdf","page":8,"moddate":"2024-02-26T13:44:25+09:00","creator":"Adobe InDesign 19.2 (Windows)"}


created_at: 2026-05-29 09:26:56
topic: persistent://default/default/fb85bf48-961e-4953-9ab3-7fbd469f8358
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encoding: FLOAT32
metadata:
{"trapped":"/False","moddate":"2024-02-26T13:44:25+09:00","creationdate":"2024-02-26T13:44:15+09:00","page":8,"creator":"Adobe InDesign 19.2 (Windows)","total_pages":23,"source":"./data/서울대 미대.pdf","producer":"Adobe PDF Library 17.0","page_label":"9","chroma:document":"생활’이 있으며, 이 수업들을 제외한 모든 디자인 전공의 수업은 \n             전공수업이다.\n• 전선 : 디자인전공 개설 수업만을 전선수업으로 인정한다. \n• 전필 : 전선 수업들 중에서도 필히 들어야 할 교과목들은 전필로 지정되어 있다.  \n             현재 디자인 학부 디자인전공의 전필교과목은  디자인기초1·2,  \n             한국미술과 문화, 서양미술사, 디자인사, 미술대학 10개 공통 과목 중  \n             4과목 선택 총 9개 교과목 21학점이다. 졸업하기 위해서는  \n             전필교과목이수가 반드시 필요하다.\n◦ 교과구분 및 전공구분\n수강신청\n3-4\n디자인기초1 / 한국미술과 문화\n미술대학 공통교과목 중 택 2 \n전공필수 교과목\n1학기\n평면조형1 / 평면조형2 / 평면조형3 / 평면조형4  \n입체조형1 / 입체조형2 \n기초도예 / 기초금속공예 \n기초산업디자인 / 기초시각디자인"}


created_at: 2026-05-29 09:26:56
topic: persistent://default/default/fb85bf48-961e-4953-9ab3-7fbd469f8358
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{"producer":"Adobe PDF Library 17.0","page_label":"9","creationdate":"2024-02-26T13:44:15+09:00","moddate":"2024-02-26T13:44:25+09:00","trapped":"/False","total_pages":23,"creator":"Adobe InDesign 19.2 (Windows)","chroma:document":"입체조형1 / 입체조형2 \n기초도예 / 기초금속공예 \n기초산업디자인 / 기초시각디자인 \n디자인기초2 / 서양미술사 / 디자인사\n미술대학 공통교과목 중 택 2 \n2학기\n평면조형1 / 평면조형2 / 평면조형3 / 평면조형4  \n입체조형1 / 입체조형2 \n기초도예 / 기초금속공예 \n기초산업디자인 / 기초시각디자인 \n졸업전시 수업 수강을 위한 세부전공별 전공필수이수교과목\n공통\n시각\n산업\n시각디자인 핵심이수교과목\n산업디자인 핵심이수교과목\n시각디자인 선택이수교과목 \n산업디자인 선택이수교과목 \n타이포그래피디자인1, UI디자인프로그래밍, 편집디자인, 정보인터랙션디자인\n타이포그래피디자인2, VD컴퓨테이션, 아이덴티티디자인, 모션그래픽스\n시각디자인기초, 애니메이션, 일러스트레이션, 영상디자인\n디자인사진, 3D그래픽스, 서비스UX디자인\n혼합매체드로잉, 사물인터랙션디자인, 바이오디자인\n혼합재료모델링, 운송디자인, 공간디자인\n디자인기초1, 디자인기초2, 디자인사","page":8,"source":"./data/서울대 미대.pdf"}


created_at: 2026-05-29 09:26:56
topic: persistent://default/default/fb85bf48-961e-4953-9ab3-7fbd469f8358
id: dce1065b-9bd8-4ebd-b60d-92be6de9aac8
encoding: FLOAT32
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{"trapped":"/False","total_pages":23,"creationdate":"2024-02-26T13:44:15+09:00","page":8,"page_label":"9","creator":"Adobe InDesign 19.2 (Windows)","producer":"Adobe PDF Library 17.0","chroma:document":"혼합재료모델링, 운송디자인, 공간디자인\n디자인기초1, 디자인기초2, 디자인사\n디자인요소와원리, 공간디자인기초, 제품서비스디자인, 리빙디자인\n제품디자인, 구조와재료디자인, 인간행태와디자인, 사회적이슈와디자인\n1학기\n2학기\n1학기\n2학기\n1학기\n2학기\n1학기\n2학기\n* 2024학번부터는 아래의 세부전공별 요건을 이수한 학생만이 졸업전시 관련 수업을 수강할 수 있음.\n* 졸업전시 이전에 학생이 해당하는 전공의 핵심이수교과목 중 최소 6과목을 반드시 포함하여,  \n  핵심이수교과목+선택이수교과목 총 8과목 이상을 이수해야 함.\n* 복수전공으로 진입한 학부생(자율전공학부생 포함)은 디자인기초1·2,디자인사 및 해당하는  \n  전공의 핵심이수(최소 6과목)+선택이수 총 8과목 이상 이수하여야만 졸업전시 관련 수업을  \n  수강할 수 있음.\n* 2024년 이전 학번의 경우, 본인 학번에 해당하는 기존의 세부전공별 요건 또는 신설된","source":"./data/서울대 미대.pdf","moddate":"2024-02-26T13:44:25+09:00"}


created_at: 2026-05-29 09:26:56
topic: persistent://default/default/fb85bf48-961e-4953-9ab3-7fbd469f8358
id: 7532518b-5311-4e58-ad39-8fc48566edca
encoding: FLOAT32
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{"creator":"Adobe InDesign 19.2 (Windows)","trapped":"/False","page":8,"page_label":"9","total_pages":23,"chroma:document":"세부전공별 요건에 포함되어 있는 개별 과목들을 자율적으로 선택하여 핵심이수(최소 6과목)+ \n  선택이수 총 8개 이상 과목을 맞추어 이수할 수 있음. (다음 페이지 참조)\n* 2024년 이전 학번의 경우, 본인 학번에 해당하는 기존의 세부전공별 요건 또는 신설된  \n  세부전공별 요건 중 한 곳이라도 핵심/선택이수교과목으로 분류되는 경우, 수강 학기와 상관  \n  없이 핵심/선택이수교과목으로 분류 가능\n* 최소 6과목 이상 이수\n* 최소 6과목 이상 이수\n* 핵심이수교과목+선택이수교과목 8과목 이상 이수\n* 핵심이수교과목+선택이수교과목 8과목 이상 이수","creationdate":"2024-02-26T13:44:15+09:00","source":"./data/서울대 미대.pdf","producer":"Adobe PDF Library 17.0","moddate":"2024-02-26T13:44:25+09:00"}


created_at: 2026-05-29 09:26:56
topic: persistent://default/default/fb85bf48-961e-4953-9ab3-7fbd469f8358
id: 92a6a864-a707-40e9-8ccb-d7875e5c1925
encoding: FLOAT32
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{"creationdate":"2024-02-26T13:44:15+09:00","creator":"Adobe InDesign 19.2 (Windows)","trapped":"/False","page":9,"page_label":"10","moddate":"2024-02-26T13:44:25+09:00","chroma:document":"18 19\n졸업전시 수업 수강을 위한 세부전공별 전공필수이수교과목\n공통\n시각\n산업\n시각디자인 핵심이수교과목\n산업디자인 핵심이수교과목\n시각디자인 선택이수교과목 \n산업디자인 선택이수교과목 \n시각디자인기초, 애니매이션, 편집디자인, 정보인터랙션디자인\n타이포그래피디자인, 미디어디자인프로그래밍, 아이덴티티디자인, 모션그래픽스\n일러스트레이션1, 영상디자인\n일러스트레이션2, 공공커뮤니케이션디자인, 미디어인터랙션디자인\n디자인과정과방법, 주거공간디자인, 사물인터랙션디자인\n운송기기디자인\n디자인기초1, 디자인기초2, 디자인사\n디자인의요소와원리, 공간디자인컨셉스터디, 제품디자인, 사회적이슈와디자인\n제품리버스디자인, 구조와재료디자인, 인간행태와디자인, 도시공간디자인\n1학기\n2학기\n1학기\n2학기\n1학기\n2학기\n1학기\n2학기\n◦ 직전 내규의 세부전공별 이수교과목 (2020~2023학번)\n* 2024년 이전 학번의 경우, 본인 학번에 해당하는 기존의 세부전공별 요건 또는 신설된","producer":"Adobe PDF Library 17.0","source":"./data/서울대 미대.pdf","total_pages":23}


created_at: 2026-05-29 09:26:56
topic: persistent://default/default/fb85bf48-961e-4953-9ab3-7fbd469f8358
id: eb6dbb43-4fff-44e1-aea6-46e8750e67b0
encoding: FLOAT32
metadata:
{"page":9,"creator":"Adobe InDesign 19.2 (Windows)","total_pages":23,"moddate":"2024-02-26T13:44:25+09:00","page_label":"10","producer":"Adobe PDF Library 17.0","source":"./data/서울대 미대.pdf","creationdate":"2024-02-26T13:44:15+09:00","trapped":"/False","chroma:document":"세부전공별 요건에 포함되어 있는 개별 과목들을 자율적으로 선택하여 핵심이수(최소 6과목)+ \n  선택이수 총 8개 이상 과목을 맞추어 이수할 수 있음.  \n* 2024년 이전 학번의 경우, 본인 학번에 해당하는 기존의 세부전공별 요건 또는 신설된  \n  세부전공별 요건 중 한 곳이라도 핵심/선택이수교과목으로 분류되는 경우, 수강 학기와 상관  \n  없이 핵심/선택이수교과목으로 분류 가능\n• 일선 : 위의 교과구분에 해당하지 않는 수업, 즉 미대 외부 타과의 전공수업들은  \n              모두 일반 선택에 해당한다. 상대적으로 미대 외부의 학생들에게는  \n              미술대학의 전공수업이 일반 선택으로 적용된다. 성적표에 미술대학  \n              수업인데 일선으로 표기되었을 경우에는 졸업하기 전에 전선 혹은 전필로  \n              교과과정 정정을 신청한다."}


created_at: 2026-05-29 09:26:56
topic: persistent://default/default/fb85bf48-961e-4953-9ab3-7fbd469f8358
id: 7e01478d-3daa-4a4f-952f-7e53b9c4ed4d
encoding: FLOAT32
metadata:
{"producer":"Adobe PDF Library 17.0","moddate":"2024-02-26T13:44:25+09:00","page":9,"chroma:document":"교과과정 정정을 신청한다.\n• 전공구분 : 주전공, 복수전공, 연계전공 등 현재 본인이 택한 전공 트랙을  \n                    의미한다. 기본적으로 디자인전공을 졸업하기 위해 듣는 수업은  \n                    모두 주전공에 해당한다. 이 외 복수전공을 이수하면서 듣는 수업은  \n                    전공구분을 복수전공으로 표기하여야 한다. 복수전공시 전공구분을  \n                    명확히 표기하여야 복수전공 이수학점이 정상적으로 전산 처리된다.  \n                    마이스누 > 누적성적 > 교과구분변경에서 전공구분을 신청한 후  \n                    출력하여 학과사무실에 제출하면 된다. \n수강예시 1 (시각디자인전공) - 핵심이수교과목 8과목\n 타이포그래피디자인1, UI디자인프로그래밍, 편집디자인, 정보인터랙션디자인,","creationdate":"2024-02-26T13:44:15+09:00","total_pages":23,"page_label":"10","trapped":"/False","creator":"Adobe InDesign 19.2 (Windows)","source":"./data/서울대 미대.pdf"}


created_at: 2026-05-29 09:26:56
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{"producer":"Adobe PDF Library 17.0","trapped":"/False","moddate":"2024-02-26T13:44:25+09:00","page_label":"10","chroma:document":"타이포그래피디자인1, UI디자인프로그래밍, 편집디자인, 정보인터랙션디자인,  \n 타이포그래피디자인2, VD컴퓨테이션, 아이덴티티디자인, 모션그래픽스\n수강예시 2 (시각디자인전공) - 핵심이수교과목 6과목, 선택이수교과목 2과목\n 타이포그래피디자인1, UI디자인프로그래밍, 편집디자인, 정보인터랙션디자인, 아이덴티티디자인,  \n 모션그래픽스, 일러스트레이션,  3D그래픽스\n수강예시 3 (시각디자인전공, 2024학번 이전) - 핵심이수교과목 6과목, 선택이수교과목 2과목\n 시각디자인기초(기존 핵심이수교과목), 타이포그래피디자인(기존 핵심이수교과목), 편집디자인,  \n 정보인터랙션디자인, 아이덴티티디자인, 모션그래픽스, 일러스트레이션,  3D그래픽스\n수강예시 4 (산업디자인전공) - 핵심이수교과목 8과목 \n 디자인요소와원리, 공간디자인기초, 제품서비스디자인, 리빙디자인, 제품디자인, 구조와재료디자인,  \n 인간행태와디자인, 사회적이슈와디자인 (핵심이수교과목 8과목)","source":"./data/서울대 미대.pdf","page":9,"total_pages":23,"creator":"Adobe InDesign 19.2 (Windows)","creationdate":"2024-02-26T13:44:15+09:00"}


created_at: 2026-05-29 09:26:56
topic: persistent://default/default/fb85bf48-961e-4953-9ab3-7fbd469f8358
id: 1ec061bc-90bb-4a40-94f3-e22c5b48afe4
encoding: FLOAT32
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{"page_label":"10","moddate":"2024-02-26T13:44:25+09:00","producer":"Adobe PDF Library 17.0","trapped":"/False","total_pages":23,"creator":"Adobe InDesign 19.2 (Windows)","chroma:document":"인간행태와디자인, 사회적이슈와디자인 (핵심이수교과목 8과목)\n수강예시 5 (산업디자인전공) - 핵심이수교과목 6과목, 선택이수교과목 2과목 \n 공간디자인기초, 제품서비스디자인, 리빙디자인, 제품디자인, 구조와재료디자인, 인간행태와디자인,  \n 바이오디자인, 운송디자인\n수강예시 5 (산업디자인전공, 2024학번 이전) - 핵심이수교과목 6과목, 선택이수교과목 2과목 \n 제품리버스디자인(기존 핵심이수교과목), 공간디자인컨셉스터디(기존 핵심이수교과목), 리빙디자인,  \n 제품디자인, 구조와재료디자인, 인간행태와디자인, 바이오디자인, 운송디자인","page":9,"source":"./data/서울대 미대.pdf","creationdate":"2024-02-26T13:44:15+09:00"}


created_at: 2026-05-29 09:26:56
topic: persistent://default/default/fb85bf48-961e-4953-9ab3-7fbd469f8358
id: 802c6cef-7888-4ed9-96c0-e0aa1f8be44c
encoding: FLOAT32
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{"creationdate":"2024-02-26T13:44:15+09:00","page_label":"11","total_pages":23,"producer":"Adobe PDF Library 17.0","trapped":"/False","page":10,"moddate":"2024-02-26T13:44:25+09:00","source":"./data/서울대 미대.pdf","chroma:document":"20 21\n학사일정표를 통해 수강신청 기간을 알 수 있으며 포털 마이스누 > 학사정보 > \n나의정보 > 종합정보 > 학사일정 페이지를 참고한다. 수강신청 기간에는 마이스누에서 \n바로 수강신청 페이지로 이 동할 수 있다. 수강 신청은 총 18학점까지 가능하며, \n직전학기 2학기 평점 3.3 이상일 경우 21학 점까지 신청 가능하다. 수강신청 시 \n교과구분, 전공구분을 명확히 하도록 한다. 교과구분은 전선, 전필, 교양을 말하는 \n것이며, 전공구분은 주전공, 복수전공, 연계전공 등을 의미한다. 수강신청 시 전공구분, \n교과구분을 명확히 하지 않으면 졸업 시 남은 학점을 정확하게 계산하기 어렵다.\n수강신청기간에 수강신청하지 못한 교과목은 개강 후 1주일 간의 변경기간 동안 \n수강정원외신청이 가능하다. sugang.snu.ac.kr > 수강신청 > 정원 외 신청 > 정원 \n외 신청서 작성을 통해 신청이 가능하며, 담당 강사의 승인을 통해 수강이 확정된다.  \n* 정원 외 신청 가능한 경우","creator":"Adobe InDesign 19.2 (Windows)"}


created_at: 2026-05-29 09:26:56
topic: persistent://default/default/fb85bf48-961e-4953-9ab3-7fbd469f8358
id: ef3d9504-241c-4989-bad5-48087bd9d61c
encoding: FLOAT32
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{"page":10,"source":"./data/서울대 미대.pdf","producer":"Adobe PDF Library 17.0","moddate":"2024-02-26T13:44:25+09:00","creationdate":"2024-02-26T13:44:15+09:00","page_label":"11","chroma:document":"* 정원 외 신청 가능한 경우\n - 수강반 제한 등으로 정원이 마감되지 않았더라도 수강신청 할 수 없는 경우\n - 정원이 마감되어 수강신청 할 수 없는 경우\n - 신청가능학점 : 수강신청가능학점 수강신청한 학점 + 6 학점\n - 수강확정가능학점 : 수강신청 가능 학점 이내\n◦ 수강신청\n◦ 수강정원외신청\n◦ 수강신청취소\n수강신청 취소는 수업주수 1/2선까지 가능하다. 정확한 날짜는 포털 마이스누 > \n학사정보 > 나의정보 > 종합정보 > 학사일정 페이지를 참고한다. 개강 후 일주일동안의 \n변경기간에는 임의로 취소가 가능하며, 변경기간이 지난 후에는 수강신청사이트가 \n아닌 마이스누에서 취소신청을 한 후, 취소원을 출력하여 담당교수님께 허락을 받고 \n과사무실에 제출한다. 디자인전공 수업의 경우에는 조교와의 협의를 통해 사인 \n과정을 생략하거나 담당 교수와의 통화를 통해서도 가능하지만, 디자인 전공이 아닌 \n외부수업의 경우 반드시 담당 강사의 사인 혹은 통화내역을 증명해야 한다.","trapped":"/False","creator":"Adobe InDesign 19.2 (Windows)","total_pages":23}


created_at: 2026-05-29 09:26:56
topic: persistent://default/default/fb85bf48-961e-4953-9ab3-7fbd469f8358
id: 8156ae4e-b7c0-4763-bc69-c59d93da0962
encoding: FLOAT32
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{"chroma:document":"외부수업의 경우 반드시 담당 강사의 사인 혹은 통화내역을 증명해야 한다.\n※취소원 제출 후에는 반드시 마이스누에서 교과목 추가 삭제여부를 확인 한다.\n개강 후 2주일이 지나면 대부분 수강신청이 확정되며, 담당 교수들은 확정 출석부를 \n통해서 출석을 확인하게 된다. 하지만, 대부분 선생님들은 확정 출석부를 사용하지 \n않고, 기존의 출석부를 통해서 출석을 확인하기 때문에 반드시 마이스누에서 본인의 \n수강신청 여부를 확인하여야 한다. 수업주수 1/2선, 즉 수강신청 취소기간이 지난 \n이후에는 어떠한 변경도 불가하므로 이후의 수강신청 관련 문제는 결국 학생의 책임이 \n된다. 따라서 철저히 개강 후 한 달 안에는 본인의 수강신청 내역을 확인하도록 한다.\n◦ 수강신청내역확인\n◦ 계절학기\n계절학기 수업은 학칙상의 정규학기와는 구분된다. 계절학기 수업에서 제공하는 \n교과목을 이수하여 취득할 수 있는 학점은 여름/9, 겨울/6학점까지이며, 취득한 학점은","creationdate":"2024-02-26T13:44:15+09:00","moddate":"2024-02-26T13:44:25+09:00","creator":"Adobe InDesign 19.2 (Windows)","source":"./data/서울대 미대.pdf","page":10,"total_pages":23,"producer":"Adobe PDF Library 17.0","trapped":"/False","page_label":"11"}


created_at: 2026-05-29 09:26:56
topic: persistent://default/default/fb85bf48-961e-4953-9ab3-7fbd469f8358
id: 449f8e63-1421-4f9c-84a3-ced0c39211f4
encoding: FLOAT32
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{"page_label":"11","total_pages":23,"source":"./data/서울대 미대.pdf","creator":"Adobe InDesign 19.2 (Windows)","page":10,"creationdate":"2024-02-26T13:44:15+09:00","producer":"Adobe PDF Library 17.0","moddate":"2024-02-26T13:44:25+09:00","chroma:document":"교과목을 이수하여 취득할 수 있는 학점은 여름/9, 겨울/6학점까지이며, 취득한 학점은 \n학칙에 규정된 학사과정 이수학점에 포함하되, 계절학기는 학칙상의 수업연한에는 \n포함하지 아니한다.\n• 수강신청기간 : 매년 5월 중, 11월중\n• 성적평점평균 : 계절학기에 취득한 교과목의 성적 평점평균은 당해 학년도의  \n                            정규학기 성적 평점평균에는 합산하지 아니하며, 졸업학점  \n                            계산에는 포함한다.","trapped":"/False"}


created_at: 2026-05-29 09:26:56
topic: persistent://default/default/fb85bf48-961e-4953-9ab3-7fbd469f8358
id: 7a71e875-d614-4a30-af17-8ff8dddac972
encoding: FLOAT32
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{"page":11,"creationdate":"2024-02-26T13:44:15+09:00","creator":"Adobe InDesign 19.2 (Windows)","producer":"Adobe PDF Library 17.0","source":"./data/서울대 미대.pdf","page_label":"12","moddate":"2024-02-26T13:44:25+09:00","total_pages":23,"chroma:document":"22 23\n등록, 성적, 휴학 등 대부분의 학사행정이 마이스누(my.snu.ac.kr)에서 이루어진다. \n따라서 마이스누 ID 및 패스워드는 반드시 기억해야 하며, 마이스누를 자주 확인하여 \n다양한 정보를 얻도록 하는 것이 좋다. \n▷ 학사행정 : 수강편람조회, 성적조회, 장학금신청, 등록 관련 조회, 휴복학 신청 등  \n                      가장 빈번하게 사용되는 메뉴\n▷ 웹메일 : 학교에서 유용한 공지가 SNU메일로 오니 확인할것.\n▷ 정보광장 : 학생 공지사항 게시판에서는 장학, 교류 등 모든 학생관련 정보를  \n                      조회할 수 있다. \n• 가입방법 : 학번이 부여된 후, 주민등록번호를 사용하여 my.snu.ac.kr에서 \n                    신청 및 발급이 가능하다. \n• 기본정보 등록 : 학교에서 취해지는 연락은 마이스누를 기초로 이뤄지니 자신의","trapped":"/False"}


created_at: 2026-05-29 09:26:56
topic: persistent://default/default/fb85bf48-961e-4953-9ab3-7fbd469f8358
id: 14792069-7fea-4bd1-aab3-76d6cca2ac83
encoding: FLOAT32
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{"producer":"Adobe PDF Library 17.0","chroma:document":"• 기본정보 등록 : 학교에서 취해지는 연락은 마이스누를 기초로 이뤄지니 자신의  \n                             연락처, 주소 등 기본 정보를 성실히 기입하고 변경하여 연락이  \n                             원활히 이뤄지도록 한다. 주소나 연락처가 바뀌는 경우 반드시  \n                             변경한다.\n• 비밀번호 분실시 : 만일 온라인을 통해서 비밀번호 파악이 안 될 경우,  \n                                중앙전산원(02-880-8282)에 직접 방문하여 본인의  \n                                신분증을 제시하고 비밀번호를 재설정해야 한다.\n◦ 마이스누\n공지확인\n3-5\n디자인전공과 관련된 장학 등의 모든 공지사항은 미술대학 홈페이지의 \n디자인전공 공지사항 게시판에 공지되므로 중요한 공지를 찾아서 보는 \n것이 중요하다. \nart.snu.ac.kr","total_pages":23,"creationdate":"2024-02-26T13:44:15+09:00","creator":"Adobe InDesign 19.2 (Windows)","page_label":"12","page":11,"moddate":"2024-02-26T13:44:25+09:00","trapped":"/False","source":"./data/서울대 미대.pdf"}


created_at: 2026-05-29 09:26:56
topic: persistent://default/default/fb85bf48-961e-4953-9ab3-7fbd469f8358
id: d5f68fb6-dff3-4a04-8e58-707e52812b27
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{"source":"./data/서울대 미대.pdf","trapped":"/False","total_pages":23,"chroma:document":"것이 중요하다. \nart.snu.ac.kr\n디자인전공과 관련된 공지사항 / 행사안내 / 공모전 / 구인 정보를 \n제공한다. 학부 내에서 긴급하게 이루어지는 학사 및 취업 관련 공지는 \n일반적으로 페이스북을 통해서 공지된다. 미술대학 홈페이지의 공지도 \n페이스북을 통해 공지되니 자주 확인하여 중요한 정보를 놓치는 일이 \n없도록 한다. \nwww.facebook.com/designsnu\n서울대학교 전체의 대표적인 학생 커뮤니티. 다른 과 학생들의 \n생각이나 동아리활동, 월세방 정보 등 모든 과 학생들의 생각을 들여다 \n볼 수 있는 곳이다. 또한 배달음식점 정보를 제공하고 있다. \nwww.snulife.com\n◦ 미술대학 홈페이지\n     (미술대학 및 디자인전공 게시판)\n◦ 디자인전공 페이스북(학부)\n◦ 스누라이프","creator":"Adobe InDesign 19.2 (Windows)","page_label":"12","moddate":"2024-02-26T13:44:25+09:00","producer":"Adobe PDF Library 17.0","page":11,"creationdate":"2024-02-26T13:44:15+09:00"}


created_at: 2026-05-29 09:26:56
topic: persistent://default/default/fb85bf48-961e-4953-9ab3-7fbd469f8358
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created_at: 2026-05-29 09:26:56
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{"creationdate":"2024-02-26T13:44:15+09:00","trapped":"/False","chroma:document":"26 27\n◆ 디자인전공 소속학생\n서울대학교의 학부 졸업기준 학점은 총 130학점이며, \n평균학점 2.00점 이상이어야 졸업 가능하다.\n디자인전공 개설 전공수업을 57학점 이상 이수하여야 하며, \n전공필수교과목을 필수로 이수해야 한다. (미술대학 \n공통교과목을 포함한 전공필수교과목은 57학점에 포함됨) \n디자인전공의 전공 학점을 69학점 이상 이수하여야 하며,  \n전공필수교과목을 필수로 이수해야 한다. (미술대학 \n공통교과목을 포함한 전공필수교과목은 69학점에 포함됨) \n교양 학점은 총 36학점 이상 이수하여야 하며 대학 글쓰기, \n대학영어를 포함한 학문의 기초 8학점 이상, 이외 학문의 기초  \n4개 분야별 3학점 이상(12학점) 이수하여야 한다.\n6학점 이상 이수하여야 하며, 어떤 강좌든 수강편람 우측에 'e' \n표시가 되어 있는 교과는 영어강의로 인정된다. 현재 디자인 \n전공의 영어강의는 'UI디자인프로그래밍' 및 '디자인사고및연구' \n강좌가 영어진행 강의에 해당한다.","producer":"Adobe PDF Library 17.0","total_pages":23,"page_label":"14","page":13,"source":"./data/서울대 미대.pdf","moddate":"2024-02-26T13:44:25+09:00","creator":"Adobe InDesign 19.2 (Windows)"}


created_at: 2026-05-29 09:26:56
topic: persistent://default/default/fb85bf48-961e-4953-9ab3-7fbd469f8358
id: 54031f51-403b-487e-9033-eeb81b79c865
encoding: FLOAT32
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{"moddate":"2024-02-26T13:44:25+09:00","trapped":"/False","total_pages":23,"chroma:document":"강좌가 영어진행 강의에 해당한다.\n졸업전시를 위해서는 각 세부전공이 정한 전공필수과목을 모두 \n이수한 후, 매년 초 4학년 졸업전시 교과를 해당 교수에게 수강 \n허락을 받고 수강하여야 한다.\n이후 각 세부전공이 정한 절차에 따라 예비발표를 진행하고, \n졸업전시 직전 담당 교수님들이 참여하시는 졸업심사를 통과한 \n자에 한하여 졸업전시가 가능하다.\n졸업기준\n4-2\n다전공 선택할 경우\n심화전공 경우\n교양학점\n영어진행강의\n졸업전시\n◆ 타전공 소속학생\n디자인전공 복수전공자\n디자인전공 부전공자\n디자인전공 개설 전공 학점을 57학점 이상 이수하여야 \n하며, 전공필수교과목을 필수로 이수해야한다. (미술대학 \n공통교과목을 포함한 전공필수교과목은 57학점에 포함됨) \n* 미술대학 내 복수전공자는 필수과목을 9학점까지 중복 인정 \n'디자인사' 교과목을 포함한 디자인전공 학점 21학점 이상 \n이수해야 한다.\n* 부전공은 1학년 전공선택수업 이수시 '기초산업디자인(입체조형(005))',","source":"./data/서울대 미대.pdf","creationdate":"2024-02-26T13:44:15+09:00","creator":"Adobe InDesign 19.2 (Windows)","page":13,"producer":"Adobe PDF Library 17.0","page_label":"14"}


created_at: 2026-05-29 09:26:56
topic: persistent://default/default/fb85bf48-961e-4953-9ab3-7fbd469f8358
id: 66722048-74ea-473f-8ded-eadb04e38958
encoding: FLOAT32
metadata:
{"chroma:document":"* 부전공은 1학년 전공선택수업 이수시 '기초산업디자인(입체조형(005))',  \n  '기초시각디자인(매체의기초(005))'만 인정함.","moddate":"2024-02-26T13:44:25+09:00","source":"./data/서울대 미대.pdf","page":13,"producer":"Adobe PDF Library 17.0","page_label":"14","creationdate":"2024-02-26T13:44:15+09:00","trapped":"/False","creator":"Adobe InDesign 19.2 (Windows)","total_pages":23}


created_at: 2026-05-29 09:26:56
topic: persistent://default/default/fb85bf48-961e-4953-9ab3-7fbd469f8358
id: 089cceef-5481-43b5-9ca6-69106c27e547
encoding: FLOAT32
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{"page":14,"creator":"Adobe InDesign 19.2 (Windows)","creationdate":"2024-02-26T13:44:15+09:00","total_pages":23,"page_label":"15","producer":"Adobe PDF Library 17.0","trapped":"/False","chroma:document":"28 29\n기 결정된 세부전공은 원칙적으로 변경될 수 없으나 특별한 사유가 있을 경우 \n세부전공분리 신청을 받는 기간에 세부전공 변경신청서 및 사유서(자유양식)을 제출한 \n후 전공교수회의에서의 심사를 통과하여야 하며, 아래 경우에 한하여 변경할 수 있다.\n1.\n2.\n3.\n◦ 세부전공 변경\n신청자격\n신청시기\n2개 학기를 이수한 학생을 대상으로 하며, 1학년 기초과정의 8개 \n필수 교과목을 모두 이수한 자. 총 이수학점이 교양과목 포함 30\n학점 이상인 자.\n매년 초 정확한 시기는 디자인과 홈페이지를 통해 공지된다. \n일년에 한 번 있으므로 2학년 진입시 반드시 공지를 참고하여 \n세부전공 분리를 신청하도록 한다.\n디자인전공에 입학한 모든 학생은 1학년 기초과정을 이수한 후 다음의 선발 절차에 \n의하여 2학년 진입 시 자신의 세부전공을 결정하여야 한다. 특정 세부전공을 \n선택한 학생수가 각 세부전공에서 정한 수용능력을 초과하는 경우에는 디자인전공","source":"./data/서울대 미대.pdf","moddate":"2024-02-26T13:44:25+09:00"}


created_at: 2026-05-29 09:26:56
topic: persistent://default/default/fb85bf48-961e-4953-9ab3-7fbd469f8358
id: 942a8fa2-ed59-49d8-a6d7-6319eaa72a27
encoding: FLOAT32
metadata:
{"trapped":"/False","moddate":"2024-02-26T13:44:25+09:00","page":14,"chroma:document":"선택한 학생수가 각 세부전공에서 정한 수용능력을 초과하는 경우에는 디자인전공 \n교수회의에서 정한 성적평점(교양과목 포함한 2개 학기 평점평균) 선발기준에 따른다. \n학생들은 소속 세부전공에서 요구하는 이수 지침을 충족시켜야 졸업할 수 있다.\n세부전공의 선택\n4-3\n* 산업디자인전공 졸업전시교과목 수강 전 선수교과\n◦ 세부전공 선수교과\n세부전공을 선택한 이후에는 각 전공별 선수과목을 이수하여야 졸업전시 교과를 \n이수하고 졸업할 수 있으며 각 세부전공별 선수과목은 다음과 같다. 졸업전시 교과목 \n수강직전 과사무실에 성적증명서와 수학계획서를 받아 전공교수에게 자격을 허락 받은 \n자 만이 졸업전시 수업을 수강할 수 있다. \n* 시각디자인전공 졸업전시교과목 수강 전 선수교과\n디자인기초1\n디자인기초2\n디자인사\n+ 졸업필수교과목 디자인사, 한국미술과문화, 서양미술사. 디자인기초1, 디자인기초 2, 미술대학공통교과목 10개 중 택 4","source":"./data/서울대 미대.pdf","creationdate":"2024-02-26T13:44:15+09:00","page_label":"15","producer":"Adobe PDF Library 17.0","creator":"Adobe InDesign 19.2 (Windows)","total_pages":23}


created_at: 2026-05-29 09:26:56
topic: persistent://default/default/fb85bf48-961e-4953-9ab3-7fbd469f8358
id: 605e6155-47ea-4048-b5c2-9c400b7b8661
encoding: FLOAT32
metadata:
{"trapped":"/False","creator":"Adobe InDesign 19.2 (Windows)","creationdate":"2024-02-26T13:44:15+09:00","source":"./data/서울대 미대.pdf","page":14,"producer":"Adobe PDF Library 17.0","total_pages":23,"chroma:document":"+ 졸업필수교과목 디자인사, 한국미술과문화, 서양미술사. 디자인기초1, 디자인기초 2, 미술대학공통교과목 10개 중 택 4\n* 졸업전시 이전에 학생이 해당하는 전공의 핵심이수교과목(6과목 포함)+선택이수교과목에서 \n  총 8과목 이상을 이수해야 함.\n* 졸업전시 이전에 학생이 해당하는 전공의 핵심이수교과목(6과목 포함)+선택이수교과목에서 \n  총 8과목 이상을 이수해야 함.\n디자인기초1\n디자인기초2\n디자인사\n전공필수이수교과목\n전공필수이수교과목\n공통\n공통\n핵심이수교과목\n핵심이수교과목\n선택이수교과목\n선택이수교과목\n졸업전시교과목\n졸업전시교과목\n그래픽디자인프로젝트1·2\n브랜드디자인프로젝트1·2\n미디어디자인프로젝트1·2\nUI,UX디자인프로젝트1·2\n제품인터랙션디자인프로젝트1·2\n모빌리티디자인프로젝트1·2\n리빙디자인프로젝트1·2\n공간디자인프로젝트1·2\n타이포그래피디자인1\n타이포그래피디자인2\nUI디자인프로그래밍\nVD컴퓨테이션\n편집디자인\n아이덴티티디자인\n정보인터랙션디자인\n모션그래픽스","moddate":"2024-02-26T13:44:25+09:00","page_label":"15"}


created_at: 2026-05-29 09:26:56
topic: persistent://default/default/fb85bf48-961e-4953-9ab3-7fbd469f8358
id: 7096a331-b49e-4498-80cc-ce240c690321
encoding: FLOAT32
metadata:
{"creator":"Adobe InDesign 19.2 (Windows)","trapped":"/False","source":"./data/서울대 미대.pdf","moddate":"2024-02-26T13:44:25+09:00","producer":"Adobe PDF Library 17.0","page_label":"15","creationdate":"2024-02-26T13:44:15+09:00","page":14,"total_pages":23,"chroma:document":"VD컴퓨테이션\n편집디자인\n아이덴티티디자인\n정보인터랙션디자인\n모션그래픽스\n시각디자인기초\n애니메이션\n디자인사진\n일러스트레이션\n영상디자인\n3D그래픽디자인\n서비스UX디자인\n디자인요소와원리\n공간디자인기초\n제품디자인\n구조와재료디자인\n제품서비스디자인\n리빙디자인\n인간행태와디자인\n사회적이슈와디자인\n혼합매체드로잉\n혼합재료모델링\n사물인터랙션디자인\n바이오디자인\n운송디자인\n공간디자인\n▶\n▶\n세부전공변경 직전 2개 학기의 전공실기 교과목 평점이 3.0 이상이어야 한다.\n학생들은 반드시 세부전공변경 신청서(미술대학 디자인전공 게시판 참고) 및\n변경사유서(자유양식)를 학부사무실에 제출한다.\n변경 신청자는 디자인전공 교수회의에서 협의하여 최종 결정한다."}


created_at: 2026-05-29 09:26:56
topic: persistent://default/default/fb85bf48-961e-4953-9ab3-7fbd469f8358
id: 050962d1-988b-4b87-829f-1f7e335e00d0
encoding: FLOAT32
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{"page_label":"16","total_pages":23,"creator":"Adobe InDesign 19.2 (Windows)","chroma:document":"30 31\n◦ 졸업전시\n◦ 졸업의 절차\n졸업전은 매년 12월에 있으며, 4학년은 졸업전시 준비위원회를 자체적으로 조직하여 \n전시 6개월 전에 과사무실에 알린다. 전시는 일반적으로 49동 내에서 진행된다. \n졸업전시에 참여하기 위해서는 세부전공 별로 졸업전시교과목을 2개 이상 이수하여  \n2개 이상의 작품을 졸업심사에 출품하고 심사를 통과하여야 한다.\n해당 학기에 졸업을 희망하는 학생들은 개강 이전 졸업사정기간에 \n학과사무실을 방문하여 졸업사정신청원 및 누적성적증명서와 마지막 \n학기 수강신청계획원을 제출하고 담당조교에게 필히 졸업 사정을 \n받아야 한다. \n교과구분이 정확히 기재되어있는 것은 졸업의 전산처리에 \n중요한 부분이다. 교과구분이 정확하지 않을 경우, 마이스누에서 \n교과구분정정신청을 한 다음 신청서를 출력하여 학과사무실에 제출.\n※교과구분 정정이 제대로 되지 않은 경우, 온라인상 졸업불가로 \n판정되며 졸업이 불가할 수 있다.","producer":"Adobe PDF Library 17.0","moddate":"2024-02-26T13:44:25+09:00","page":15,"source":"./data/서울대 미대.pdf","creationdate":"2024-02-26T13:44:15+09:00","trapped":"/False"}


created_at: 2026-05-29 09:26:56
topic: persistent://default/default/fb85bf48-961e-4953-9ab3-7fbd469f8358
id: 6acf63ab-effe-4e0d-805f-919e5f442f4f
encoding: FLOAT32
metadata:
{"trapped":"/False","page_label":"16","source":"./data/서울대 미대.pdf","producer":"Adobe PDF Library 17.0","creationdate":"2024-02-26T13:44:15+09:00","moddate":"2024-02-26T13:44:25+09:00","total_pages":23,"chroma:document":"판정되며 졸업이 불가할 수 있다.\n졸업신청기간에 마이스누에서 졸업을 신청한 다음 졸업신청서를 \n과사무실에 제출한다. \n시각디자인전공 졸업전시 교과목\n(4과목 중 2과목 선택)\n그래픽디자인프로젝트1·2\n브랜드디자인프로젝트1·2\n미디어디자인프로젝트1·2\nUI,UX디자인프로젝트1·2\n산업디자인전공 졸업전시 교과목\n(4과목 중 2과목 선택)\n제품인터랙션디자인프로젝트1·2\n모빌리티디자인프로젝트1·2\n리빙디자인프로젝트1·2\n공간디자인프로젝트1·2\n졸업사정\n(~개강첫주)\n교과구분정정\n졸업신청\n졸업전시 및 졸업\n4-4\n전과란, 본교 내에서 학생의 학과(부) 소속을 변경하여 다른 학과(부)에 소속함을 \n말한다. 전과(부)는 매 학년도 말에 한하여 4회 이상 등록하고 제2학년 수료학점 (66\n학점) 이상, 제3학년 수료 학점 (98학점)에 미달되는 자로 한다. 전 · 출입 인원은 학과(\n부)별 입학 정원의 100분의 20 이내로 한다. 사범계 이외의 학과(부)는 사범계 이외의","creator":"Adobe InDesign 19.2 (Windows)","page":15}


created_at: 2026-05-29 09:26:56
topic: persistent://default/default/fb85bf48-961e-4953-9ab3-7fbd469f8358
id: 6a50f2ef-3012-4db7-b52f-6dd55beae41c
encoding: FLOAT32
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{"page_label":"16","moddate":"2024-02-26T13:44:25+09:00","producer":"Adobe PDF Library 17.0","creator":"Adobe InDesign 19.2 (Windows)","trapped":"/False","page":15,"total_pages":23,"creationdate":"2024-02-26T13:44:15+09:00","source":"./data/서울대 미대.pdf","chroma:document":"각 학과(부)간 (다만 의학계, 치의학계, 약학계, 수의학계, 간호학계로의 전과(부)는 \n제외) 전과가 가능하다.\n전과(부)를 하고자 하는 자는 소정의 원서와 소속 대학장의 승인서, \n성적증명서를 첨부하여 지원대학장에게 제출한다.\n전과(부)를 하고자 하는 자는 소속학과(부)장의 승인서와 \n성적증명서를 첨부하여 소정의 원서를 소속대학장에게 제출한다.\n전과(부)한 자의 재학연한은 종전의 재학연한을 통산한다.\n전과(부)후의 교과목 이수는 전입학과(부)의 교과목 이수 원칙을 \n따른다. 전과(부)는 1개 학과(부)에 한하여 지원 가능함 \n(이중지원 불가)\n소속대학 외\n소속대학 내\n유의사항\n전과\n4-5"}


created_at: 2026-05-29 09:26:56
topic: persistent://default/default/fb85bf48-961e-4953-9ab3-7fbd469f8358
id: 48dc3d99-aefa-4a80-845d-4267e4b7d7bf
encoding: FLOAT32
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{"total_pages":23,"creationdate":"2024-02-26T13:44:15+09:00","page_label":"17","trapped":"/False","producer":"Adobe PDF Library 17.0","chroma:document":"32 33\n2007년 이후에 입학한 학생들은 제2전공 및 심화전공이 필수사항이므로, 위의 \n학점배분표에 기준하여 제2전공 혹은 심화전공을 선택하도록 하며, 별도로 제2\n전공을 신청하지 않았을 경우 자동으로 심화전공으로 결정된다. 복수, 연합, 부전공의 \n이수기준은 전적으로 해당학과의 기준을 따른다. 따라서 제2전공 선택시 해당학과에 \n문의하여 이수기준을 반드시 확인하여야 한다.\n복수전공이란 학생들의 교과목 선택의 폭을 넓히고 지식의 편협함을 지양하여 넓은 \n학문적 시야를 갖출 수 있도록 하는 것으로써, 소속 전공과정을 포함하여 2개 이상의 \n전공과정을 이수하는 것을 말한다.\n복수전공이수자는 해당 학과(부)에서 정한 전공과목 학점을 이수하여야 하고, 성적 \n평점평균이 2.7 이상이어야 한다. 복수전공시 복수전공 학과(부)의 교과과정에서 \n전공교과목으로 인정하는 경우(단과대학 내의 공통교과목 포함)에는 9학점까지, 소속","source":"./data/서울대 미대.pdf","page":16,"moddate":"2024-02-26T13:44:25+09:00","creator":"Adobe InDesign 19.2 (Windows)"}


created_at: 2026-05-29 09:26:56
topic: persistent://default/default/fb85bf48-961e-4953-9ab3-7fbd469f8358
id: 79909e6b-d768-4b4a-bbac-b5be1e5fe61b
encoding: FLOAT32
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{"moddate":"2024-02-26T13:44:25+09:00","source":"./data/서울대 미대.pdf","page":16,"page_label":"17","total_pages":23,"trapped":"/False","chroma:document":"전공교과목으로 인정하는 경우(단과대학 내의 공통교과목 포함)에는 9학점까지, 소속 \n전공학과(부)와 복수전공 학과(부)의 교과과정 에서 공통으로 인정하는 타 학과(부) \n교과목(단과대학 내의 공통교과목 포함)은 3학점까지 중복 인정할 수 있다.\n학부.학과(학과(부)내 전공과정 포함)를 원칙으로 함.\n학과(부)의 졸업에 필요한 최저학점의 2분의 1 이상 취득한 이후\n개학 한 달전 공지 참고하여 복수전공 신청서와 구비서류를 소속 학과\n(부)장의 승인을 받아 단과대학 내 복수전공인 경우에는 학장에게, \n단과대학간 복수전공인 경우에는 학장을 경유하여 복수전공을 \n이수코자 하는 대학에 제출한다.\n◦ 복수전공\n적용범위\n지원자격\n신청절차\n부전공, 복수전공, 연합전공, 교직\n4-6\n교양 (36) 복수전공\n(전공별내규) 자유\n전공 (57)\n전필 :21학점포함\n전선 :디자인개설과목","creationdate":"2024-02-26T13:44:15+09:00","creator":"Adobe InDesign 19.2 (Windows)","producer":"Adobe PDF Library 17.0"}


created_at: 2026-05-29 09:26:56
topic: persistent://default/default/fb85bf48-961e-4953-9ab3-7fbd469f8358
id: 0d7829f2-fdee-4e50-af06-c6f34c1554ce
encoding: FLOAT32
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{"moddate":"2024-02-26T13:44:25+09:00","chroma:document":"(전공별내규) 자유\n전공 (57)\n전필 :21학점포함\n전선 :디자인개설과목\n0                    36                                         93                    130\n부전공이란 오늘날 학문이 점차 상호 교류를 지향하고 있는 추세에 비추어서 자기 전공 \n이외 분야에 대한 학생들의 학구 의욕을 충족시키기 위하여 타 학과 전공과목을 일정 \n수 이상 체계적으로 이수하는 과정을 말한다. 따라서 현재 전공하는 분야에 만족하지 \n못하거나 다양성 있게 학문을 하고자 하는 경우 부전공을 이수하는 것이 좋을 것이다.\n사범대학 각 학과, 농업생명과학대학 농산업교육과 및 교직과정이 설치된 학과 \n상호간에 가능하며, 이외의 학과(부) 학생들은 위 학과의 부전공을 이수할 수는 있으나 \n교원자격증은 취득할 수 없다. 단, 교원자격증의 부전공 표기를 원하는 경우는 부전공 \n학과의 교과교육론 및 교과교재 연구 및 지도법을 포함한다.","page":16,"creator":"Adobe InDesign 19.2 (Windows)","producer":"Adobe PDF Library 17.0","source":"./data/서울대 미대.pdf","page_label":"17","creationdate":"2024-02-26T13:44:15+09:00","total_pages":23,"trapped":"/False"}


created_at: 2026-05-29 09:26:56
topic: persistent://default/default/fb85bf48-961e-4953-9ab3-7fbd469f8358
id: 5849466d-c6c8-497c-b6f5-da3458f3662e
encoding: FLOAT32
metadata:
{"trapped":"/False","producer":"Adobe PDF Library 17.0","page_label":"17","source":"./data/서울대 미대.pdf","moddate":"2024-02-26T13:44:25+09:00","chroma:document":"학과의 교과교육론 및 교과교재 연구 및 지도법을 포함한다.\n인문·사회계 각 학과(부)와 자연과학대학의 수리과학부, 통계학과 및 \n사범대학 수학교육과\n인문·사회과학·자연과학계 대학 전 학과(부) \n소속대학의 각 학과(부)\n의예·수의예·의학·치의학·수의학·간호학과는 부전공을 이수할 수 \n없으며, 타 학과(부) 학생도 위 학과를 부전공 할 수 없음.\n33학점 이상 취득자\n개학 한 달전 공지 참고하여 부전공 지원서와 성적증명서를 소속 학과\n(부)장의 승인을 받아 학장을 경유, 부전공을 이수하고자 하는 대학에 \n제출한다.\n◦ 부전공\n※ 교원자격증 취득과 관련된 부전공 이수\n인문사회계\n자연계\n예능계\n불가학과\n지원자격\n신청절차\n교양 (36)\n부전공\n연계전공\n(전공별내규)\n자유\n전공 (57)\n전필 :21학점포함\n전선 :디자인개설과목\n0                    36                                         93                    130","creationdate":"2024-02-26T13:44:15+09:00","creator":"Adobe InDesign 19.2 (Windows)","total_pages":23,"page":16}


created_at: 2026-05-29 09:26:56
topic: persistent://default/default/fb85bf48-961e-4953-9ab3-7fbd469f8358
id: 00876415-46b0-44ef-b837-cbdc1f964d33
encoding: FLOAT32
metadata:
{"page":17,"total_pages":23,"trapped":"/False","source":"./data/서울대 미대.pdf","producer":"Adobe PDF Library 17.0","creationdate":"2024-02-26T13:44:15+09:00","moddate":"2024-02-26T13:44:25+09:00","creator":"Adobe InDesign 19.2 (Windows)","page_label":"18","chroma:document":"34 35\n2개 이상의 학과(부) 전공이 연합하여 교과과정을 편성, 제공하거나 학생이 교과과정을 \n구성하여 대학의 인정을 받은 것을 말한다.\n4개 학기 이상 6개 학기 이내 이수한 경우인 자. 직전학기 성적 \n평점평균으로 2.7 이상.\n연합전공 참여학과 재학생.\n부전공 및 복수전공 이수자는 지원할 수 없음.\n연합전공 해당 학부(과)에서 정한 전공학점을 최소 39학점 이상 \n이수(단, 각 전공에 중복되는 교과목은 인정하지 않음)하고, 성적 \n평점평균이 2.0 이상이어야 한다. \n개학 한달 전 공지를 참고하여 지도교수, 학과장, 해당 대학을 경유하여 \n연합전공 주임에게 신청서 제출.\n취소원은 매학기 개시 2주 전 연합전공 주임에게 제출.\n◦ 연합전공\n지원자격\n지원자격\n신청절차\n교양 (36) 연합전공\n(전공별내규) 자유\n전공 (57)\n전필 :21학점포함\n전선 :디자인개설과목"}


created_at: 2026-05-29 09:26:56
topic: persistent://default/default/fb85bf48-961e-4953-9ab3-7fbd469f8358
id: eb51f49d-bf25-48f8-98a2-bf8c0d476ba3
encoding: FLOAT32
metadata:
{"creationdate":"2024-02-26T13:44:15+09:00","moddate":"2024-02-26T13:44:25+09:00","page_label":"18","page":17,"chroma:document":"(전공별내규) 자유\n전공 (57)\n전필 :21학점포함\n전선 :디자인개설과목\n0                    36                                         93                    130\n복수전공 이수 시 소속 학과와 복수전공 학과의 교과과정에서 전공과목으로 공통 \n인정하는 과목 (단과대학 내에서 공통과목 포함)은 9학점까지, 소속 학과(부)와 \n복수전공 학과(부)의 교과과정에서 공통 인정하는 타 학과(부)의 과목은 3학점까지 \n전공학점의 이수로 중복하여 인정할 수 있다.\n연합전공의 이수 시 소속 학과(부)에서 연합전공 교과과정에 제공한 과목은  \n9학점까지, 소속 학과 (부)와 연합전공의 교과과정에서 공통으로 인정하는 타 학과 \n(부) 과목은 3학점까지 전공학점의 이 수로 중복하여 인정할 수 있다.\n부전공 이수 시에는 과목을 중복하여 인정하지 아니한다.  \n단, 먼저 2과목까지는 선수로 듣는 것은 허용된다.","producer":"Adobe PDF Library 17.0","total_pages":23,"trapped":"/False","creator":"Adobe InDesign 19.2 (Windows)","source":"./data/서울대 미대.pdf"}


created_at: 2026-05-29 09:26:56
topic: persistent://default/default/fb85bf48-961e-4953-9ab3-7fbd469f8358
id: f71e8523-8f60-43fc-bdbd-292b0328a31f
encoding: FLOAT32
metadata:
{"chroma:document":"단, 먼저 2과목까지는 선수로 듣는 것은 허용된다.\n*** 미술대학 내에서 복수전공 시에는 1학년 기초 전필과목을 대상으로  \n        총 9학점을 인정한다.\n전공과목의 중복인정\n4-7","page":17,"moddate":"2024-02-26T13:44:25+09:00","creator":"Adobe InDesign 19.2 (Windows)","source":"./data/서울대 미대.pdf","page_label":"18","producer":"Adobe PDF Library 17.0","creationdate":"2024-02-26T13:44:15+09:00","trapped":"/False","total_pages":23}


created_at: 2026-05-29 09:26:56
topic: persistent://default/default/fb85bf48-961e-4953-9ab3-7fbd469f8358
id: c670780e-1a88-4bf4-beee-a2ae1dc07f06
encoding: FLOAT32
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{"source":"./data/서울대 미대.pdf","creationdate":"2024-02-26T13:44:15+09:00","page_label":"19","producer":"Adobe PDF Library 17.0","chroma:document":"36 37\n자리배정은 개강 직전인 2월말과 8월말 두 차례 전 학년이 모여 학기의 시작과 함께 \n준비한다. 함께 쓰는 49동의 유지와 관리를 위한 것으로 반드시 참여해야 하는 \n최소한의 수칙이다. 1학기는 2월말, 2학기는 8월말로 하되, 모이는 시간과 장소는 \n과사무실에서 공지한다. 참여하지 않은 학생은 자리 배치, 실험실습기자재 지원 등의 \n학과 혜택에서 불이익을 받을 수 있다.\n건물 출입은 학생증을 통해 가능하며, 출입 등록이 안되어 있을 경우, 50동 \n서무행정실에 방문하여 등록한다.\n◦ 학기전 자리배정 및 실기실 안전교육 (년2회)\n◦ 출입 등록\n디자인연구동(49동)\n5-1\n장비의 대여는 원칙적으로 디자인전공 학생이 수업을 위해 사용하는 용도로만 대여가 \n가능하다. 장비 대여시에는 반드시 학생증을 지참하고 학과사무실에 학생증을 맡기고, \n학과 장비 대여 장부에 기입한 후, 장비를 대여한다.","creator":"Adobe InDesign 19.2 (Windows)","trapped":"/False","page":18,"moddate":"2024-02-26T13:44:25+09:00","total_pages":23}


created_at: 2026-05-29 09:26:56
topic: persistent://default/default/fb85bf48-961e-4953-9ab3-7fbd469f8358
id: 1c759fb6-ba54-48c1-8f7d-7dcb0ace32cb
encoding: FLOAT32
metadata:
{"source":"./data/서울대 미대.pdf","page":18,"moddate":"2024-02-26T13:44:25+09:00","producer":"Adobe PDF Library 17.0","total_pages":23,"chroma:document":"학과 장비 대여 장부에 기입한 후, 장비를 대여한다.\n장비는 최장 하루를 넘지 않도록 하며, 절대 교외로 반출되지 않도록 한다. 만약 반출 \n시 적발 될 경우에는 차후 장비 대여에 대한 제한을 받을 수 있다. 장비 대여시 장비에 \n이상이 없는지, 구성품을 확인하여 고장 유무를 확인한다. 분실 및 장비 훼손 시에는 \n학생에게 상당의 책임을 물을 수 있다.\n◦ 장비 대여 및 반납\n디자인전공의 전시장은 49동 S&D홀, 215호, 215-1호와 220동 지하 전시실을 사용할 \n수 있으며 그 밖에 전시 공간은 협의 하에 사용할 수 있다.\n디자인연구동 전시장은 학기 중의 과제전, 졸업전, 공식적인 행사 등의 요청에 의해 \n우선적으로 사용할 수 있으며, 그 밖의 개인 전시 등은 위의 일정을 피해 사용할 수 \n있다. 전시장의 사용은 과사무실에 사용신청서를 제출하여 허가 후 사용한다.\n전시장을 사용하고 난 후에는 청소 및 벽면 상태에 대한 검사를 맡아야 하며, 그에 따른","trapped":"/False","page_label":"19","creationdate":"2024-02-26T13:44:15+09:00","creator":"Adobe InDesign 19.2 (Windows)"}


created_at: 2026-05-29 09:26:56
topic: persistent://default/default/fb85bf48-961e-4953-9ab3-7fbd469f8358
id: 6fe0ddea-d934-4ce1-94f0-4352483ec043
encoding: FLOAT32
metadata:
{"total_pages":23,"creator":"Adobe InDesign 19.2 (Windows)","page_label":"19","page":18,"producer":"Adobe PDF Library 17.0","source":"./data/서울대 미대.pdf","creationdate":"2024-02-26T13:44:15+09:00","chroma:document":"전시장을 사용하고 난 후에는 청소 및 벽면 상태에 대한 검사를 맡아야 하며, 그에 따른 \n청소 상태 불량에 대한 책임을 질 의무가 있다.\n디자인연구동의 전산실 109호는 수업시간 이외에는 자유롭게 사용할 수 있으며 \n야간 잔류 시에는 신청서를 작성한 후 사용한다. 전산실의 컴퓨터는 공용 컴퓨터로써 \n불법소프트웨어의 설치가 금지되므로 각별한 주의를 요한다. 또한 여러 명이 공동으로 \n사용하는 컴퓨터이므로 필요한 작업을 수행한 후에는 컴퓨터의 파일과 주변을 정리 \n정돈 한다. 또한 전산실에는 중앙전산원에서 관리하는 흑백 A4프린터기가 있어서 \n복사카드를 사용하여 출력이 가능하다.\n전시장\n전산실\n5-2\n5-3\n5. 학사일반","trapped":"/False","moddate":"2024-02-26T13:44:25+09:00"}


created_at: 2026-05-29 09:26:56
topic: persistent://default/default/fb85bf48-961e-4953-9ab3-7fbd469f8358
id: 8b25d2a2-0cc6-4a54-a70a-eea427f54074
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{"trapped":"/False","chroma:document":"38 39\n디자인연구동의 사진 스튜디오는 지하에 위치해 있으며, 학과사무실에서 미리 신청서 \n작성, 보증금 3만원을 지급한 후, 키를 지급받아 사용할 수 있다. 관리상의 문제로 사진 \n스튜디오는 디자인 전공 구성원 중 졸업전시 과목 준비에 관한 용무만 허가한다. \n사진 스튜디오의 사용 후 조교 및 근로장학생의 검사를 받는다. 만약 기물이 \n고장나거나, 스튜디오 배경에 발자국 등 사용 상태가 불량하다고 판단되면 예치금은 \n환급하지 않으며 기물 수리, 청소 등의 책임을 져야 한다.\n사진 스튜디오\n5-4\n모형제작실을 사용할 시에는 안전과 직접적으로 관련된 안전수칙을 충분히 숙지하고 \n안전수칙에 따라 작업한다. 실제로 위험한 기계를 사용하다가 부상을 당하는 경우가 \n많으므로 작업시에 무엇보다 안전에 유의한다. \n※ 디자인학부 모형제작실 안전수칙\n• 실습장내의 기계를 사용할 때에는 반드시 직원(트렌드아이디)의 입회 또는 감독     \n   하에서만 사용할 수 있다.","source":"./data/서울대 미대.pdf","page":19,"creator":"Adobe InDesign 19.2 (Windows)","producer":"Adobe PDF Library 17.0","total_pages":23,"page_label":"20","moddate":"2024-02-26T13:44:25+09:00","creationdate":"2024-02-26T13:44:15+09:00"}


created_at: 2026-05-29 09:26:56
topic: persistent://default/default/fb85bf48-961e-4953-9ab3-7fbd469f8358
id: 55222fb2-2db9-4238-a99a-fd1bdc7dd8f8
encoding: FLOAT32
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{"creator":"Adobe InDesign 19.2 (Windows)","moddate":"2024-02-26T13:44:25+09:00","producer":"Adobe PDF Library 17.0","creationdate":"2024-02-26T13:44:15+09:00","trapped":"/False","total_pages":23,"chroma:document":"하에서만 사용할 수 있다.\n• 실습장 내(內)에서는 반드시 단순한 작업복장으로 출입하여야 한다.\n   (끈이 길게 달린 복장, 긴 모발 등은 안전사고를 유발할 있으므로 특히 주의한다.)\n• 실습장내에서는 타인의 작업에 방해가 되지 않도록 하여야 한다.\n• 각종 기계류를 사용할 때에는 반드시 규정된 안전수칙과 작업 요령을 숙지한 후   \n   소중하게 사용하여야 한다.\n• 각종 수공구를 사용한 후에는 반드시 지정된 장소에 가져다 놓아야 한다.\n• 절대 화기 접근을 금한다.\n• 작업이 모두 끝난 후에는 주변을 깨끗이 청소한다.\n• 일과시간 외에 부득이하게 야간작업이 필요할 때는 수위실에 신고한 후 사용한다.\n모형제작실\n5-5","source":"./data/서울대 미대.pdf","page_label":"20","page":19}


created_at: 2026-05-29 09:26:56
topic: persistent://default/default/fb85bf48-961e-4953-9ab3-7fbd469f8358
id: cba59f9c-27f9-4636-896d-1dd060eddcc6
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{"chroma:document":"40 41\n학과의 실습실의 리스트는 아래와 같으며, 각 학년에 소속된 학과 실습실을 사용하는 \n것을 원칙으로 한다. 학생들이 공동으로 사용하는 공간인만큼 책임감을 가지고 공간을 \n정리하면서 사용하도록 한다. \n제2전공의 전공생도 학과 실습실의 공간을 사용할 수 있으며, 이 때 각과의 구성원들과 \n합의 하에 자리 및 사물함을 배정받을 수 있다.\n1학년\n2학년\n3학년 VD\n3학년 ID\n4학년 VD\n4학년 ID\n52동 402호\n49동 112호\n49동 210호\n49동 212호\n49동 211호\n49동 213호\n과방(학생실기실)\n5-6\n시기\n1월초\n2월중\n2월중\n3월\n3월초\n2월말\n4월초\n4월중\n8월말\n11월초\n12월초\n12월초\n2월말\n대상\n동문, 교수, 졸업생\n전공주임,조교,신입생\n신입생,재학생,조교\n신입생, 교수\n신입생, 교수\n학부생. 대학원생\n학부생, 교수\n학부생 전체, 교수\n학부생. 대학원생\n디자인전공 구성원 \n귀빈,교수,졸업대상자\n졸업대상자\n졸업확정자\n내용","producer":"Adobe PDF Library 17.0","creator":"Adobe InDesign 19.2 (Windows)","creationdate":"2024-02-26T13:44:15+09:00","page_label":"21","total_pages":23,"page":20,"source":"./data/서울대 미대.pdf","trapped":"/False","moddate":"2024-02-26T13:44:25+09:00"}


created_at: 2026-05-29 09:26:56
topic: persistent://default/default/fb85bf48-961e-4953-9ab3-7fbd469f8358
id: 990ec617-afff-448f-bd7f-4442af10eed9
encoding: FLOAT32
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{"producer":"Adobe PDF Library 17.0","total_pages":23,"creationdate":"2024-02-26T13:44:15+09:00","page_label":"21","source":"./data/서울대 미대.pdf","moddate":"2024-02-26T13:44:25+09:00","page":20,"chroma:document":"학부생. 대학원생\n디자인전공 구성원 \n귀빈,교수,졸업대상자\n졸업대상자\n졸업확정자\n내용\n학교생활의 기본이 되는 학사 및 \n기타 정보를 교육\n미술대학 신입생 전체 엠티\n본부행사 / 체육관\n미술대학전체 교수소개 / 미술관\n자리배정 및 실기실 안전교육\n학부 전공진입을 축하하는 자리\n디자인전공 총엠티(전체참석)\n자리배정 및 실기실 안전교육\n디자인전공 개최 국제학술대회\n졸업전시 전 귀빈 초청\n졸업전시회 개최\n본부행사 / 체육관\n행사명\n신년하례식\n신입생오리엔테이션\n새내기 새로배움터\n입학식\n미술대학 오리엔테이션\n실기실 안전교육\n전공진입식\n총엠티(스케치여행)\n실기실 안전교육\nIDCC\n졸업전시프리뷰\n졸업전시\n졸업식\n6. 연간행사","creator":"Adobe InDesign 19.2 (Windows)","trapped":"/False"}


created_at: 2026-05-29 09:26:56
topic: persistent://default/default/fb85bf48-961e-4953-9ab3-7fbd469f8358
id: d24cea16-a620-41fb-b361-74f1ffd11dbe
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{"moddate":"2024-02-26T13:44:25+09:00","chroma:document":"초안 | 이장섭 교수, 이경태 조교\n수정 | 이장섭 교수, 이경태 조교\n발행일 | 2024년 2월\n주소 | 서울 관악구 관악로 1 서울대학교 49동 디자인연구동 101호\n연락처 | 02.880.7512/7463","creationdate":"2024-02-26T13:44:15+09:00","producer":"Adobe PDF Library 17.0","source":"./data/서울대 미대.pdf","page_label":"22","creator":"Adobe InDesign 19.2 (Windows)","total_pages":23,"trapped":"/False","page":21}


created_at: 2026-05-29 09:27:06
topic: persistent://default/default/d67716a2-d18d-440e-8134-5f8eeb23f334
id: b99993a6-253b-4ff0-8e60-7741e58f3a5f
encoding: FLOAT32
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{"moddate":"2023-04-05T00:33:07+00:00","creator":"LaTeX with hyperref","creationdate":"2023-04-05T00:33:07+00:00","page":34,"keywords":"","title":"","total_pages":35,"page_label":"35","subject":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","author":"","trapped":"/False","producer":"pdfTeX-1.40.21","chroma:document":"Shot Sim-to-Real Transfer of Visual Grasping Models. IEEE Robotics\nand Automation Letters. 2023.\n35","source":"./data/Summary of ChatGPTGPT-4 Research.pdf"}


created_at: 2026-05-29 09:32:40
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: 846dc7cf-83ba-4e43-9e70-c5df40087d07
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{"author":"","subject":"","creator":"LaTeX with hyperref","page":0,"source":"./data/Summary of ChatGPTGPT-4 Research.pdf","title":"","page_label":"1","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","total_pages":35,"keywords":"","producer":"pdfTeX-1.40.21","trapped":"/False","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"}


created_at: 2026-05-29 09:32:40
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: 33e9f1c9-f77d-4d2b-952b-05ef948294ef
encoding: FLOAT32
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{"subject":"","page_label":"1","creator":"LaTeX with hyperref","keywords":"","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","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","title":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","total_pages":35,"author":"","page":0,"trapped":"/False","moddate":"2023-04-05T00:33:07+00:00","creationdate":"2023-04-05T00:33:07+00:00"}


created_at: 2026-05-29 09:32:40
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: eea996e6-e2a7-43d2-bf0c-72e9ee6faa6c
encoding: FLOAT32
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{"source":"./data/Summary of ChatGPTGPT-4 Research.pdf","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,"author":"","page_label":"1","page":0,"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":"","trapped":"/False","title":"","producer":"pdfTeX-1.40.21","moddate":"2023-04-05T00:33:07+00:00","creator":"LaTeX with hyperref"}


created_at: 2026-05-29 09:32:40
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: d2b04c1e-17ae-470c-a3df-df8356a912d5
encoding: FLOAT32
metadata:
{"author":"","page":1,"source":"./data/Summary of ChatGPTGPT-4 Research.pdf","creationdate":"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","trapped":"/False","producer":"pdfTeX-1.40.21","keywords":"","moddate":"2023-04-05T00:33:07+00:00","page_label":"2","subject":"","title":"","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","total_pages":35}


created_at: 2026-05-29 09:32:40
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: eca435fd-75a0-4e94-9207-4dcb4c901b73
encoding: FLOAT32
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{"page":1,"trapped":"/False","subject":"","title":"","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-","page_label":"2","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","total_pages":35,"producer":"pdfTeX-1.40.21","keywords":"","creationdate":"2023-04-05T00:33:07+00:00","moddate":"2023-04-05T00:33:07+00:00","author":""}


created_at: 2026-05-29 09:32:40
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: 5a206e9d-0847-4e4f-85a2-bb86a0477ab6
encoding: FLOAT32
metadata:
{"keywords":"","trapped":"/False","author":"","page":1,"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","moddate":"2023-04-05T00:33:07+00:00","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","subject":"","page_label":"2","creator":"LaTeX with hyperref","title":"","creationdate":"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"}


created_at: 2026-05-29 09:32:40
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: efb52711-a25a-41b0-a3ef-88764b758c4e
encoding: FLOAT32
metadata:
{"keywords":"","creationdate":"2023-04-05T00:33:07+00:00","title":"","total_pages":35,"producer":"pdfTeX-1.40.21","subject":"","page_label":"2","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","trapped":"/False","page":1,"creator":"LaTeX with hyperref","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","source":"./data/Summary of ChatGPTGPT-4 Research.pdf"}


created_at: 2026-05-29 09:32:40
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: e8b488cb-8725-4277-9564-96b16f9add81
encoding: FLOAT32
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{"subject":"","page_label":"3","title":"","chroma:document":"Figure 1: The number of papers submitted by researchers per day.\n3","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","trapped":"/False","producer":"pdfTeX-1.40.21","creationdate":"2023-04-05T00:33:07+00:00","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","total_pages":35,"keywords":"","page":2,"moddate":"2023-04-05T00:33:07+00:00","author":""}


created_at: 2026-05-29 09:32:40
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: 22ac09e9-79fe-4b22-adab-0e31c2c5cfb0
encoding: FLOAT32
metadata:
{"creator":"LaTeX with hyperref","author":"","total_pages":35,"creationdate":"2023-04-05T00:33:07+00:00","title":"","producer":"pdfTeX-1.40.21","keywords":"","trapped":"/False","moddate":"2023-04-05T00:33:07+00:00","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","page_label":"4","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","page":3,"source":"./data/Summary of ChatGPTGPT-4 Research.pdf","subject":""}


created_at: 2026-05-29 09:32:40
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: 577bc616-08cb-429d-b0b0-97c8fe39e436
encoding: FLOAT32
metadata:
{"creationdate":"2023-04-05T00:33:07+00:00","page_label":"4","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","creator":"LaTeX with hyperref","author":"","trapped":"/False","producer":"pdfTeX-1.40.21","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","page":3,"title":"","subject":"","total_pages":35,"keywords":""}


created_at: 2026-05-29 09:32:40
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: 68d95e4f-bc89-4033-b29c-47209527def2
encoding: FLOAT32
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{"title":"","moddate":"2023-04-05T00:33:07+00:00","creationdate":"2023-04-05T00:33:07+00:00","page_label":"5","page":4,"creator":"LaTeX with hyperref","total_pages":35,"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","subject":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","chroma:document":"Figure 3: The distribution of submitted papers across various ﬁelds.\n5","keywords":""}


created_at: 2026-05-29 09:32:40
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: 853ad609-da39-484e-b23c-da9c228e0b3b
encoding: FLOAT32
metadata:
{"page":5,"creator":"LaTeX with hyperref","subject":"","keywords":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","total_pages":35,"author":"","trapped":"/False","producer":"pdfTeX-1.40.21","moddate":"2023-04-05T00:33:07+00:00","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","page_label":"6","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: 2026-05-29 09:32:40
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: 5d28bb36-3200-4788-a361-e42f92a3fcfd
encoding: FLOAT32
metadata:
{"page":5,"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-","producer":"pdfTeX-1.40.21","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","creationdate":"2023-04-05T00:33:07+00:00","keywords":"","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":"","subject":"","author":"","creator":"LaTeX with hyperref","trapped":"/False","moddate":"2023-04-05T00:33:07+00:00","page_label":"6"}


created_at: 2026-05-29 09:32:40
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: 9765b006-2772-4adb-bd98-35b1a75a260f
encoding: FLOAT32
metadata:
{"total_pages":35,"source":"./data/Summary of ChatGPTGPT-4 Research.pdf","moddate":"2023-04-05T00:33:07+00:00","page_label":"6","trapped":"/False","author":"","page":5,"keywords":"","creator":"LaTeX with hyperref","producer":"pdfTeX-1.40.21","title":"","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","creationdate":"2023-04-05T00:33:07+00:00","subject":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2"}


created_at: 2026-05-29 09:32:40
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: 9848f2ac-2ee4-45be-a5bc-5b504c384269
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","total_pages":35,"moddate":"2023-04-05T00:33:07+00:00","author":"","page_label":"6","creationdate":"2023-04-05T00:33:07+00:00","title":"","producer":"pdfTeX-1.40.21","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","trapped":"/False","subject":"","page":5,"creator":"LaTeX with hyperref","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","keywords":""}


created_at: 2026-05-29 09:32:40
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: a9590d41-4db9-442e-808c-919b00f3901e
encoding: FLOAT32
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{"moddate":"2023-04-05T00:33:07+00:00","page":6,"trapped":"/False","subject":"","creator":"LaTeX with hyperref","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","total_pages":35,"source":"./data/Summary of ChatGPTGPT-4 Research.pdf","page_label":"7","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","producer":"pdfTeX-1.40.21","author":"","title":""}


created_at: 2026-05-29 09:32:40
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: 5f2510c6-8f61-46fd-bb3c-012f204fe113
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{"subject":"","title":"","author":"","creator":"LaTeX with hyperref","moddate":"2023-04-05T00:33:07+00:00","total_pages":35,"page_label":"7","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","page":6,"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","creationdate":"2023-04-05T00:33:07+00:00","trapped":"/False","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":""}


created_at: 2026-05-29 09:32:40
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: cd37fbdd-532b-49e0-8d9a-ed5ddd06151e
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","total_pages":35,"page":6,"subject":"","trapped":"/False","page_label":"7","title":"","creator":"LaTeX with hyperref","creationdate":"2023-04-05T00:33:07+00:00","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","moddate":"2023-04-05T00:33:07+00:00","keywords":"","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","producer":"pdfTeX-1.40.21"}


created_at: 2026-05-29 09:32:40
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: 173370c8-6f27-4b61-91f0-43716d26cfcd
encoding: FLOAT32
metadata:
{"total_pages":35,"page":6,"creator":"LaTeX with hyperref","creationdate":"2023-04-05T00:33:07+00:00","producer":"pdfTeX-1.40.21","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","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","title":"","trapped":"/False","keywords":"","page_label":"7","moddate":"2023-04-05T00:33:07+00:00","author":"","subject":""}


created_at: 2026-05-29 09:32:40
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: 35576913-58d3-4cfc-88cb-c392a1205838
encoding: FLOAT32
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{"page_label":"8","trapped":"/False","subject":"","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","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,"producer":"pdfTeX-1.40.21","creator":"LaTeX with hyperref","keywords":"","creationdate":"2023-04-05T00:33:07+00:00","moddate":"2023-04-05T00:33:07+00:00","title":""}


created_at: 2026-05-29 09:32:40
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: 81d708ec-d576-41db-a5df-f8ff9639bcc3
encoding: FLOAT32
metadata:
{"source":"./data/Summary of ChatGPTGPT-4 Research.pdf","total_pages":35,"page_label":"8","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","producer":"pdfTeX-1.40.21","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","page":7,"creationdate":"2023-04-05T00:33:07+00:00","keywords":"","subject":"","creator":"LaTeX with hyperref","author":"","trapped":"/False"}


created_at: 2026-05-29 09:32:40
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: 57e32694-8df4-4114-a6bd-02022831bee5
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{"creator":"LaTeX with hyperref","title":"","moddate":"2023-04-05T00:33:07+00:00","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","page_label":"8","creationdate":"2023-04-05T00:33:07+00:00","trapped":"/False","producer":"pdfTeX-1.40.21","total_pages":35,"subject":"","page":7,"ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","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-","author":""}


created_at: 2026-05-29 09:32:40
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: 7bbccdb3-4740-4a17-a680-0f7510998843
encoding: FLOAT32
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{"author":"","page":7,"source":"./data/Summary of ChatGPTGPT-4 Research.pdf","title":"","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","keywords":"","creator":"LaTeX with hyperref","subject":"","moddate":"2023-04-05T00:33:07+00:00","creationdate":"2023-04-05T00:33:07+00:00","trapped":"/False","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"}


created_at: 2026-05-29 09:32:40
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: 93dfe28c-c342-45ee-afcc-d66843ff093d
encoding: FLOAT32
metadata:
{"title":"","subject":"","page":8,"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","moddate":"2023-04-05T00:33:07+00:00","trapped":"/False","creationdate":"2023-04-05T00:33:07+00:00","author":"","producer":"pdfTeX-1.40.21","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","page_label":"9","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","keywords":"","total_pages":35}


created_at: 2026-05-29 09:32:40
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: f021a9d5-17da-4302-bb5a-c986b78e4535
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{"trapped":"/False","title":"","moddate":"2023-04-05T00:33:07+00:00","author":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","page_label":"9","creationdate":"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","page":8,"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","keywords":"","total_pages":35,"subject":"","creator":"LaTeX with hyperref"}


created_at: 2026-05-29 09:32:40
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: 88776cf7-81f4-41e5-a14c-41d4dc1c5938
encoding: FLOAT32
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{"source":"./data/Summary of ChatGPTGPT-4 Research.pdf","page":8,"page_label":"9","producer":"pdfTeX-1.40.21","author":"","moddate":"2023-04-05T00:33:07+00:00","title":"","keywords":"","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:","creationdate":"2023-04-05T00:33:07+00:00","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,"creator":"LaTeX with hyperref"}


created_at: 2026-05-29 09:32:40
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: 247c4db6-939b-44d5-b62a-d9ba6e2c26a6
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metadata:
{"source":"./data/Summary of ChatGPTGPT-4 Research.pdf","total_pages":35,"ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","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","title":"","creationdate":"2023-04-05T00:33:07+00:00","keywords":"","creator":"LaTeX with hyperref","producer":"pdfTeX-1.40.21","moddate":"2023-04-05T00:33:07+00:00","subject":"","trapped":"/False","page":8,"page_label":"9","author":""}


created_at: 2026-05-29 09:32:40
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: 2613344f-9f9b-4104-a842-df0fee35351a
encoding: FLOAT32
metadata:
{"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":"","total_pages":35,"creator":"LaTeX with hyperref","moddate":"2023-04-05T00:33:07+00:00","page_label":"10","creationdate":"2023-04-05T00:33:07+00:00","subject":"","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","author":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","page":9,"title":"","trapped":"/False"}


created_at: 2026-05-29 09:32:40
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: 751ff9c6-4543-4eb0-a0a1-ffa11359f85c
encoding: FLOAT32
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{"subject":"","creator":"LaTeX with hyperref","title":"","moddate":"2023-04-05T00:33:07+00:00","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","page_label":"10","page":9,"author":"","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","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","total_pages":35}


created_at: 2026-05-29 09:32:40
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: e9e23b5c-29c7-49f0-83bf-9e0959bf59cb
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{"creationdate":"2023-04-05T00:33:07+00:00","trapped":"/False","author":"","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","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","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","keywords":"","title":"","subject":"","page":9,"page_label":"10","moddate":"2023-04-05T00:33:07+00:00","total_pages":35}


created_at: 2026-05-29 09:32:40
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: 8a96e46d-93e4-4da2-bedb-c4c64afd5454
encoding: FLOAT32
metadata:
{"creationdate":"2023-04-05T00:33:07+00:00","creator":"LaTeX with hyperref","author":"","page_label":"10","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","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","moddate":"2023-04-05T00:33:07+00:00","producer":"pdfTeX-1.40.21","subject":"","total_pages":35,"page":9,"title":"","trapped":"/False","keywords":""}


created_at: 2026-05-29 09:32:40
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: da4289eb-9136-4a0d-963c-1e08b0d45edb
encoding: FLOAT32
metadata:
{"trapped":"/False","page":10,"creationdate":"2023-04-05T00:33:07+00:00","total_pages":35,"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","moddate":"2023-04-05T00:33:07+00:00","author":"","page_label":"11","subject":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","keywords":"","producer":"pdfTeX-1.40.21","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"}


created_at: 2026-05-29 09:32:40
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: d76a5078-17fd-4f59-8ce5-c09bb6368a87
encoding: FLOAT32
metadata:
{"creationdate":"2023-04-05T00:33:07+00:00","subject":"","total_pages":35,"title":"","moddate":"2023-04-05T00:33:07+00:00","keywords":"","page_label":"11","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.","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","creator":"LaTeX with hyperref","page":10,"trapped":"/False","author":""}


created_at: 2026-05-29 09:32:40
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: 59754fbd-4ac2-47b3-8bce-1d2229b7657e
encoding: FLOAT32
metadata:
{"moddate":"2023-04-05T00:33:07+00:00","title":"","producer":"pdfTeX-1.40.21","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","page_label":"11","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-","keywords":"","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","trapped":"/False","creationdate":"2023-04-05T00:33:07+00:00","total_pages":35,"page":10,"author":""}


created_at: 2026-05-29 09:32:40
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: 0ec57823-9353-4116-8a55-6dd398224345
encoding: FLOAT32
metadata:
{"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","trapped":"/False","creator":"LaTeX with hyperref","moddate":"2023-04-05T00:33:07+00:00","title":"","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","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","keywords":"","page":10,"subject":"","author":"","creationdate":"2023-04-05T00:33:07+00:00","page_label":"11"}


created_at: 2026-05-29 09:32:40
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: 98a8e8ed-be15-47a9-a10e-b17f76ecf35d
encoding: FLOAT32
metadata:
{"creator":"LaTeX with hyperref","producer":"pdfTeX-1.40.21","trapped":"/False","subject":"","page_label":"12","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","creationdate":"2023-04-05T00:33:07+00:00","author":"","moddate":"2023-04-05T00:33:07+00:00","keywords":"","title":"","total_pages":35,"page":11,"ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","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"}


created_at: 2026-05-29 09:32:40
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: ae0991cc-c317-45e2-b0a5-af1bb2b6c965
encoding: FLOAT32
metadata:
{"page":11,"source":"./data/Summary of ChatGPTGPT-4 Research.pdf","subject":"","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","trapped":"/False","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","creator":"LaTeX with hyperref","creationdate":"2023-04-05T00:33:07+00:00","author":"","title":"","total_pages":35,"page_label":"12","producer":"pdfTeX-1.40.21"}


created_at: 2026-05-29 09:32:40
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: 8b111802-4e89-4a94-b69a-261700d83d1c
encoding: FLOAT32
metadata:
{"source":"./data/Summary of ChatGPTGPT-4 Research.pdf","creator":"LaTeX with hyperref","subject":"","trapped":"/False","keywords":"","title":"","page":11,"author":"","moddate":"2023-04-05T00:33:07+00:00","page_label":"12","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,"creationdate":"2023-04-05T00:33:07+00:00","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"}


created_at: 2026-05-29 09:32:40
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: e7e3b5c1-1cdd-445a-9940-ac0fd774f72d
encoding: FLOAT32
metadata:
{"source":"./data/Summary of ChatGPTGPT-4 Research.pdf","moddate":"2023-04-05T00:33:07+00:00","total_pages":35,"page":11,"creationdate":"2023-04-05T00:33:07+00:00","trapped":"/False","creator":"LaTeX with hyperref","keywords":"","page_label":"12","author":"","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","subject":"","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: 2026-05-29 09:32:40
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: 52d329e6-7d2f-43e4-acb5-55bedf336db2
encoding: FLOAT32
metadata:
{"moddate":"2023-04-05T00:33:07+00:00","total_pages":35,"author":"","trapped":"/False","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":"","page":12,"page_label":"13","subject":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","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","title":"","creator":"LaTeX with hyperref"}


created_at: 2026-05-29 09:32:40
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: 0defa527-ec2e-4f7f-905d-35ac0a3310c2
encoding: FLOAT32
metadata:
{"title":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","producer":"pdfTeX-1.40.21","trapped":"/False","author":"","page_label":"13","subject":"","moddate":"2023-04-05T00:33:07+00:00","total_pages":35,"creationdate":"2023-04-05T00:33:07+00:00","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","page":12,"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-"}


created_at: 2026-05-29 09:32:40
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: efbb0548-8f32-4e13-9613-d7a14f8f80ea
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{"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","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","creationdate":"2023-04-05T00:33:07+00:00","page_label":"13","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","page":12,"total_pages":35,"moddate":"2023-04-05T00:33:07+00:00","creator":"LaTeX with hyperref","subject":"","title":"","keywords":"","trapped":"/False"}


created_at: 2026-05-29 09:32:40
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: bcb3a6f1-0263-4b55-b978-4a4c605d7acc
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{"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":12,"producer":"pdfTeX-1.40.21","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","moddate":"2023-04-05T00:33:07+00:00","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","subject":"","trapped":"/False","creationdate":"2023-04-05T00:33:07+00:00","title":"","creator":"LaTeX with hyperref","page_label":"13","author":""}


created_at: 2026-05-29 09:32:40
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: d538d97a-f0f6-471f-8381-9f4cf81ac003
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{"page_label":"14","producer":"pdfTeX-1.40.21","creationdate":"2023-04-05T00:33:07+00:00","author":"","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","total_pages":35,"page":13,"title":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","trapped":"/False","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","subject":"","creator":"LaTeX with hyperref"}


created_at: 2026-05-29 09:32:40
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: e7fe695d-9f94-4858-a474-d8dc52c2b06f
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{"author":"","trapped":"/False","title":"","total_pages":35,"page_label":"14","creator":"LaTeX with hyperref","producer":"pdfTeX-1.40.21","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","creationdate":"2023-04-05T00:33:07+00:00","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","subject":"","page":13,"moddate":"2023-04-05T00:33:07+00:00"}


created_at: 2026-05-29 09:32:40
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: 732745bf-fcd0-4120-bbb7-883bb63751bb
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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","subject":"","creator":"LaTeX with hyperref","author":"","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","title":"","page":13,"moddate":"2023-04-05T00:33:07+00:00","creationdate":"2023-04-05T00:33:07+00:00","keywords":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","page_label":"14","trapped":"/False","producer":"pdfTeX-1.40.21"}


created_at: 2026-05-29 09:32:40
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: d8ad3396-1412-45c4-ad2f-e932a9ee71b5
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{"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","total_pages":35,"producer":"pdfTeX-1.40.21","title":"","creator":"LaTeX with hyperref","creationdate":"2023-04-05T00:33:07+00:00","trapped":"/False","moddate":"2023-04-05T00:33:07+00:00","subject":"","page":13,"author":"","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"}


created_at: 2026-05-29 09:32:40
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: 5e58626c-1a13-4ae8-8cc8-c1aa3adbc922
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{"title":"","trapped":"/False","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","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","author":"","subject":"","keywords":"","total_pages":35,"page":14,"moddate":"2023-04-05T00:33:07+00:00","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","producer":"pdfTeX-1.40.21","creator":"LaTeX with hyperref","page_label":"15","creationdate":"2023-04-05T00:33:07+00:00"}


created_at: 2026-05-29 09:32:40
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: 385e1194-2284-4689-8a62-0e40f9c2df34
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{"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","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":"","page":14,"page_label":"15","total_pages":35,"creator":"LaTeX with hyperref","producer":"pdfTeX-1.40.21","trapped":"/False","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","moddate":"2023-04-05T00:33:07+00:00","keywords":"","title":"","subject":""}


created_at: 2026-05-29 09:32:40
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: 76654d93-ac12-4e3a-8a35-e34955dc8163
encoding: FLOAT32
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{"creator":"LaTeX with hyperref","keywords":"","author":"","page_label":"15","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","title":"","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-","trapped":"/False","total_pages":35,"source":"./data/Summary of ChatGPTGPT-4 Research.pdf","subject":"","creationdate":"2023-04-05T00:33:07+00:00","moddate":"2023-04-05T00:33:07+00:00","producer":"pdfTeX-1.40.21","page":14}


created_at: 2026-05-29 09:32:40
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: bfec7409-9283-43c9-ad29-b744570f4ae8
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{"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","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","page_label":"15","producer":"pdfTeX-1.40.21","creator":"LaTeX with hyperref","page":14,"creationdate":"2023-04-05T00:33:07+00:00","subject":"","total_pages":35,"keywords":"","moddate":"2023-04-05T00:33:07+00:00","author":""}


created_at: 2026-05-29 09:32:40
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: aee05bdb-1f0e-41ec-b9a9-e755d0072107
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{"page":15,"author":"","title":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","keywords":"","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-","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":"16","trapped":"/False","creationdate":"2023-04-05T00:33:07+00:00","subject":"","moddate":"2023-04-05T00:33:07+00:00","creator":"LaTeX with hyperref","total_pages":35}


created_at: 2026-05-29 09:32:40
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: 900c9537-86e3-47e8-b0e2-ea69e14bc6f1
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","subject":"","keywords":"","page":15,"trapped":"/False","producer":"pdfTeX-1.40.21","page_label":"16","title":"","total_pages":35,"creationdate":"2023-04-05T00:33:07+00:00","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","author":"","creator":"LaTeX with hyperref","moddate":"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"}


created_at: 2026-05-29 09:32:40
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: 108ea33c-df84-49ef-a456-ca3daf666b90
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{"page":15,"creationdate":"2023-04-05T00:33:07+00:00","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","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","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","total_pages":35,"subject":"","page_label":"16","title":"","producer":"pdfTeX-1.40.21","trapped":"/False","keywords":"","moddate":"2023-04-05T00:33:07+00:00","author":""}


created_at: 2026-05-29 09:32:40
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: e3b769b4-113a-4aaa-b36f-0865a6b16dfd
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{"title":"","subject":"","author":"","page":15,"creator":"LaTeX with hyperref","creationdate":"2023-04-05T00:33:07+00:00","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","trapped":"/False","total_pages":35,"keywords":"","moddate":"2023-04-05T00:33:07+00:00","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","producer":"pdfTeX-1.40.21","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: 2026-05-29 09:32:40
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: 5f31e813-6d5b-4346-89c6-e04af674c450
encoding: FLOAT32
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{"creationdate":"2023-04-05T00:33:07+00:00","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","author":"","subject":"","keywords":"","creator":"LaTeX with hyperref","total_pages":35,"title":"","moddate":"2023-04-05T00:33:07+00:00","page":16,"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":"17","trapped":"/False","source":"./data/Summary of ChatGPTGPT-4 Research.pdf"}


created_at: 2026-05-29 09:32:40
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: 766bad35-04cd-406d-95a3-7eaf93e0fd47
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{"moddate":"2023-04-05T00:33:07+00:00","creator":"LaTeX with hyperref","page":16,"title":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","subject":"","author":"","total_pages":35,"source":"./data/Summary of ChatGPTGPT-4 Research.pdf","producer":"pdfTeX-1.40.21","keywords":"","creationdate":"2023-04-05T00:33:07+00:00","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","trapped":"/False","page_label":"17"}


created_at: 2026-05-29 09:32:40
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: b0de1eac-a195-4170-95da-fca1ada4db57
encoding: FLOAT32
metadata:
{"moddate":"2023-04-05T00:33:07+00:00","title":"","page_label":"17","total_pages":35,"creator":"LaTeX with hyperref","page":16,"source":"./data/Summary of ChatGPTGPT-4 Research.pdf","author":"","creationdate":"2023-04-05T00:33:07+00:00","producer":"pdfTeX-1.40.21","trapped":"/False","keywords":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","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,","subject":""}


created_at: 2026-05-29 09:32:40
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: 48ec688e-865b-4112-b978-5cc70386dfc5
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{"page":16,"total_pages":35,"moddate":"2023-04-05T00:33:07+00:00","producer":"pdfTeX-1.40.21","creationdate":"2023-04-05T00:33:07+00:00","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","keywords":"","subject":"","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","creator":"LaTeX with hyperref","trapped":"/False","author":"","page_label":"17"}


created_at: 2026-05-29 09:32:40
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: df247e5e-d689-4766-ab7a-18aa6ec6f8dc
encoding: FLOAT32
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{"source":"./data/Summary of ChatGPTGPT-4 Research.pdf","title":"","total_pages":35,"moddate":"2023-04-05T00:33:07+00:00","creator":"LaTeX with hyperref","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","keywords":"","trapped":"/False","page":17,"subject":"","page_label":"18","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"}


created_at: 2026-05-29 09:32:40
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: 9af2125d-8ba7-44b0-861a-4c3ad5f28a04
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{"creationdate":"2023-04-05T00:33:07+00:00","subject":"","title":"","keywords":"","author":"","page_label":"18","page":17,"producer":"pdfTeX-1.40.21","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","creator":"LaTeX with hyperref","trapped":"/False","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,"source":"./data/Summary of ChatGPTGPT-4 Research.pdf"}


created_at: 2026-05-29 09:32:40
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: 85529606-7fb3-40f1-b069-2356fca706a2
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{"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,"source":"./data/Summary of ChatGPTGPT-4 Research.pdf","title":"","creator":"LaTeX with hyperref","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":"","moddate":"2023-04-05T00:33:07+00:00","total_pages":35,"page_label":"18","trapped":"/False","keywords":"","producer":"pdfTeX-1.40.21","subject":""}


created_at: 2026-05-29 09:32:40
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: cea57153-bdc5-4a9f-9c02-b651521dafc2
encoding: FLOAT32
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{"author":"","title":"","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","creationdate":"2023-04-05T00:33:07+00:00","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","page":17,"producer":"pdfTeX-1.40.21","total_pages":35,"keywords":"","page_label":"18","subject":"","trapped":"/False","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"}


created_at: 2026-05-29 09:32:40
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: db7627ef-e4d8-4206-bed6-a5719a9ddad8
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","producer":"pdfTeX-1.40.21","page":18,"keywords":"","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","title":"","creationdate":"2023-04-05T00:33:07+00:00","total_pages":35,"creator":"LaTeX with hyperref","author":"","subject":"","moddate":"2023-04-05T00:33:07+00:00","trapped":"/False","page_label":"19"}


created_at: 2026-05-29 09:32:40
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: 737fddd6-5d27-46e7-940c-3d458734b885
encoding: FLOAT32
metadata:
{"creationdate":"2023-04-05T00:33:07+00:00","page":18,"author":"","subject":"","producer":"pdfTeX-1.40.21","total_pages":35,"page_label":"19","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","trapped":"/False","moddate":"2023-04-05T00:33:07+00:00","title":"","keywords":"","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-"}


created_at: 2026-05-29 09:32:40
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: 3ef6e6ab-3fa9-4769-a651-bdbe9aee5341
encoding: FLOAT32
metadata:
{"moddate":"2023-04-05T00:33:07+00:00","page_label":"19","creator":"LaTeX with hyperref","total_pages":35,"page":18,"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":"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","creationdate":"2023-04-05T00:33:07+00:00","subject":"","trapped":"/False","title":"","keywords":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","author":""}


created_at: 2026-05-29 09:32:40
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: a15d1680-88f3-47b7-b522-eb628110d96f
encoding: FLOAT32
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{"page_label":"19","subject":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","page":18,"producer":"pdfTeX-1.40.21","creator":"LaTeX with hyperref","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","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","creationdate":"2023-04-05T00:33:07+00:00","keywords":"","total_pages":35,"title":"","author":"","trapped":"/False","moddate":"2023-04-05T00:33:07+00:00"}


created_at: 2026-05-29 09:32:40
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: 30c45715-95e4-4e9b-9384-43adae9f5eac
encoding: FLOAT32
metadata:
{"author":"","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","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-","page_label":"20","page":19,"total_pages":35,"creationdate":"2023-04-05T00:33:07+00:00","trapped":"/False","producer":"pdfTeX-1.40.21","title":"","keywords":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf"}


created_at: 2026-05-29 09:32:40
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: 72c95d3c-0ed0-449f-b003-4c587f6bbb4e
encoding: FLOAT32
metadata:
{"creationdate":"2023-04-05T00:33:07+00:00","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","moddate":"2023-04-05T00:33:07+00:00","subject":"","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","total_pages":35,"trapped":"/False","producer":"pdfTeX-1.40.21","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","title":"","page":19,"page_label":"20","keywords":""}


created_at: 2026-05-29 09:32:40
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: c9c3ec5c-5768-486d-8e8c-4e6712afc6fc
encoding: FLOAT32
metadata:
{"producer":"pdfTeX-1.40.21","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","title":"","author":"","moddate":"2023-04-05T00:33:07+00:00","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","page_label":"20","page":19,"trapped":"/False","subject":"","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,"creationdate":"2023-04-05T00:33:07+00:00"}


created_at: 2026-05-29 09:32:40
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: fe8abede-9b86-4c95-ba19-48ef6f798ebd
encoding: FLOAT32
metadata:
{"total_pages":35,"creator":"LaTeX with hyperref","page":19,"page_label":"20","producer":"pdfTeX-1.40.21","subject":"","moddate":"2023-04-05T00:33:07+00:00","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","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","keywords":"","trapped":"/False","title":"","author":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf"}


created_at: 2026-05-29 09:32:40
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: 61b405f6-f480-459c-b2ba-bd0bb48e3b60
encoding: FLOAT32
metadata:
{"producer":"pdfTeX-1.40.21","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","keywords":"","page":20,"source":"./data/Summary of ChatGPTGPT-4 Research.pdf","creator":"LaTeX with hyperref","trapped":"/False","subject":"","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":"","author":"","page_label":"21","moddate":"2023-04-05T00:33:07+00:00"}


created_at: 2026-05-29 09:32:40
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: fb9c24df-c9e5-4510-91c8-7601b1a074f3
encoding: FLOAT32
metadata:
{"creationdate":"2023-04-05T00:33:07+00:00","trapped":"/False","author":"","subject":"","title":"","page_label":"21","moddate":"2023-04-05T00:33:07+00:00","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.","producer":"pdfTeX-1.40.21","page":20,"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,"source":"./data/Summary of ChatGPTGPT-4 Research.pdf","keywords":""}


created_at: 2026-05-29 09:32:40
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: d1fbdc22-e35e-4575-97c5-f13dd1c8bb34
encoding: FLOAT32
metadata:
{"title":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","creationdate":"2023-04-05T00:33:07+00:00","creator":"LaTeX with hyperref","trapped":"/False","page":20,"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,","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":"","author":"","moddate":"2023-04-05T00:33:07+00:00","total_pages":35,"page_label":"21"}


created_at: 2026-05-29 09:32:40
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: 97b76eb0-99d3-49b8-b220-ce2902f8bc44
encoding: FLOAT32
metadata:
{"subject":"","trapped":"/False","author":"","creationdate":"2023-04-05T00:33:07+00:00","moddate":"2023-04-05T00:33:07+00:00","page_label":"21","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","total_pages":35,"keywords":"","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","title":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf"}


created_at: 2026-05-29 09:32:40
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: 56cdaa38-7651-413b-b8a8-047d0be1d90b
encoding: FLOAT32
metadata:
{"producer":"pdfTeX-1.40.21","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","creator":"LaTeX with hyperref","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","creationdate":"2023-04-05T00:33:07+00:00","author":"","total_pages":35,"keywords":"","title":"","trapped":"/False","page":21,"page_label":"22","source":"./data/Summary of ChatGPTGPT-4 Research.pdf"}


created_at: 2026-05-29 09:32:40
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: 70e2b821-2b21-4a58-b929-f8dd883d3f5c
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{"author":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","subject":"","page_label":"22","creationdate":"2023-04-05T00:33:07+00:00","total_pages":35,"creator":"LaTeX with hyperref","title":"","trapped":"/False","page":21,"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":"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","moddate":"2023-04-05T00:33:07+00:00","keywords":""}


created_at: 2026-05-29 09:32:40
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: 8a25d504-9c20-4bab-a262-d5febb7ef1d5
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{"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","total_pages":35,"subject":"","page_label":"22","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","creationdate":"2023-04-05T00:33:07+00:00","moddate":"2023-04-05T00:33:07+00:00","trapped":"/False","keywords":"","producer":"pdfTeX-1.40.21","page":21,"title":""}


created_at: 2026-05-29 09:32:40
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: 99a016e6-dbdb-4230-89fc-2410fd7cbcf2
encoding: FLOAT32
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{"page":21,"keywords":"","page_label":"22","producer":"pdfTeX-1.40.21","creationdate":"2023-04-05T00:33:07+00:00","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","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","title":"","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":"","total_pages":35,"trapped":"/False"}


created_at: 2026-05-29 09:32:40
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: 0e5687e5-7231-4062-b550-b3856359ec17
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{"title":"","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","creator":"LaTeX with hyperref","producer":"pdfTeX-1.40.21","keywords":"","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","creationdate":"2023-04-05T00:33:07+00:00","subject":"","page":22,"moddate":"2023-04-05T00:33:07+00:00","trapped":"/False","page_label":"23","source":"./data/Summary of ChatGPTGPT-4 Research.pdf"}


created_at: 2026-05-29 09:32:40
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: 957fdb88-3382-4569-a1eb-a8ce739967fb
encoding: FLOAT32
metadata:
{"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","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","subject":"","total_pages":35,"page_label":"23","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","keywords":"","creator":"LaTeX with hyperref","producer":"pdfTeX-1.40.21","moddate":"2023-04-05T00:33:07+00:00","title":"","trapped":"/False","author":"","page":22}


created_at: 2026-05-29 09:32:40
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: f76adb1c-95be-4eac-a560-dfefe949f559
encoding: FLOAT32
metadata:
{"title":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","creator":"LaTeX with hyperref","trapped":"/False","producer":"pdfTeX-1.40.21","total_pages":35,"creationdate":"2023-04-05T00:33:07+00:00","author":"","page_label":"23","subject":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","page":22,"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","moddate":"2023-04-05T00:33:07+00:00","keywords":""}


created_at: 2026-05-29 09:32:40
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: 2617a2d1-a450-43e1-b637-487189b6041a
encoding: FLOAT32
metadata:
{"page_label":"23","creationdate":"2023-04-05T00:33:07+00:00","producer":"pdfTeX-1.40.21","title":"","keywords":"","creator":"LaTeX with hyperref","subject":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","page":22,"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","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","author":"","trapped":"/False"}


created_at: 2026-05-29 09:32:40
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: 4e6834cf-2cde-4678-bf44-4faea8d7745a
encoding: FLOAT32
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{"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","page":23,"creator":"LaTeX with hyperref","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","trapped":"/False","page_label":"24","moddate":"2023-04-05T00:33:07+00:00","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","keywords":"","producer":"pdfTeX-1.40.21","author":"","creationdate":"2023-04-05T00:33:07+00:00","subject":""}


created_at: 2026-05-29 09:32:40
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: 176392a1-fb79-485f-8880-2d0db9a2afe3
encoding: FLOAT32
metadata:
{"author":"","trapped":"/False","page":23,"producer":"pdfTeX-1.40.21","title":"","subject":"","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","keywords":"","creator":"LaTeX with hyperref","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","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","page_label":"24","creationdate":"2023-04-05T00:33:07+00:00"}


created_at: 2026-05-29 09:32:40
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: b5246086-3c28-414b-8237-3e1511fd7859
encoding: FLOAT32
metadata:
{"moddate":"2023-04-05T00:33:07+00:00","page":23,"total_pages":35,"producer":"pdfTeX-1.40.21","trapped":"/False","author":"","creator":"LaTeX with hyperref","creationdate":"2023-04-05T00:33:07+00:00","keywords":"","page_label":"24","title":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","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","subject":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2"}


created_at: 2026-05-29 09:32:40
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: 6f7d2ae6-89b8-4263-829f-e0571c00fe36
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","moddate":"2023-04-05T00:33:07+00:00","page":24,"page_label":"25","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","total_pages":35,"creationdate":"2023-04-05T00:33:07+00:00","title":"","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.","keywords":"","producer":"pdfTeX-1.40.21","subject":"","creator":"LaTeX with hyperref","author":""}


created_at: 2026-05-29 09:32:40
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: 2142fefb-a5dd-4db9-abf1-35027cece803
encoding: FLOAT32
metadata:
{"source":"./data/Summary of ChatGPTGPT-4 Research.pdf","producer":"pdfTeX-1.40.21","creationdate":"2023-04-05T00:33:07+00:00","author":"","moddate":"2023-04-05T00:33:07+00:00","page":24,"creator":"LaTeX with hyperref","keywords":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","trapped":"/False","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.","title":"","page_label":"25","total_pages":35,"subject":""}


created_at: 2026-05-29 09:32:40
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: e14aa27f-e17b-4cb3-a3cd-8e0a43773d65
encoding: FLOAT32
metadata:
{"subject":"","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","page":24,"creator":"LaTeX with hyperref","trapped":"/False","author":"","keywords":"","page_label":"25","title":"","producer":"pdfTeX-1.40.21","moddate":"2023-04-05T00:33:07+00:00","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","total_pages":35,"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"}


created_at: 2026-05-29 09:32:40
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: b1162d17-f6f0-4e86-becc-e81eb0d7fa93
encoding: FLOAT32
metadata:
{"page":25,"creationdate":"2023-04-05T00:33:07+00:00","author":"","producer":"pdfTeX-1.40.21","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","keywords":"","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":"","page_label":"26","trapped":"/False","title":"","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","moddate":"2023-04-05T00:33:07+00:00"}


created_at: 2026-05-29 09:32:40
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: 0429fbe2-f594-47c8-acbf-3763279d78dc
encoding: FLOAT32
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{"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","moddate":"2023-04-05T00:33:07+00:00","producer":"pdfTeX-1.40.21","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","creationdate":"2023-04-05T00:33:07+00:00","total_pages":35,"title":"","page":25,"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","page_label":"26","trapped":"/False","keywords":""}


created_at: 2026-05-29 09:32:40
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: 6111920c-3a0f-486c-a320-fddfb1f0f79a
encoding: FLOAT32
metadata:
{"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","producer":"pdfTeX-1.40.21","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","subject":"","trapped":"/False","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-","page_label":"26","creationdate":"2023-04-05T00:33:07+00:00","keywords":"","total_pages":35,"creator":"LaTeX with hyperref","author":"","page":25,"title":""}


created_at: 2026-05-29 09:32:40
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: 72c4b89d-9bbb-4100-8f58-6fa75e951e17
encoding: FLOAT32
metadata:
{"ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","title":"","creationdate":"2023-04-05T00:33:07+00:00","subject":"","page":25,"author":"","trapped":"/False","total_pages":35,"source":"./data/Summary of ChatGPTGPT-4 Research.pdf","moddate":"2023-04-05T00:33:07+00:00","producer":"pdfTeX-1.40.21","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","keywords":"","page_label":"26","creator":"LaTeX with hyperref"}


created_at: 2026-05-29 09:32:40
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: 974eabd1-1c8e-4173-b621-213d81020533
encoding: FLOAT32
metadata:
{"subject":"","trapped":"/False","moddate":"2023-04-05T00:33:07+00:00","creator":"LaTeX with hyperref","total_pages":35,"source":"./data/Summary of ChatGPTGPT-4 Research.pdf","page":26,"keywords":"","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.","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","producer":"pdfTeX-1.40.21","author":"","page_label":"27"}


created_at: 2026-05-29 09:32:40
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{"moddate":"2023-04-05T00:33:07+00:00","trapped":"/False","page_label":"27","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-","author":"","page":26,"keywords":"","title":"","total_pages":35,"producer":"pdfTeX-1.40.21","creator":"LaTeX with hyperref","creationdate":"2023-04-05T00:33:07+00:00","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"}


created_at: 2026-05-29 09:32:40
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
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created_at: 2026-05-29 09:32:40
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
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created_at: 2026-05-29 09:32:40
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
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created_at: 2026-05-29 09:32:40
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created_at: 2026-05-29 09:32:40
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created_at: 2026-05-29 09:32:40
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
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created_at: 2026-05-29 09:32:40
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
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created_at: 2026-05-29 09:32:40
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: ea4eebbd-689e-4370-8ec3-44ea6c9a368e
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created_at: 2026-05-29 09:32:40
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
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created_at: 2026-05-29 09:32:40
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
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created_at: 2026-05-29 09:32:40
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
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created_at: 2026-05-29 09:32:40
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created_at: 2026-05-29 09:32:40
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
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{"chroma:document":"2015.\n[58] Hoﬀmann R, Zhang C, Ling X, Zettlemoyer L, Weld DS. Knowledge-based\nweak supervision for information extraction of overlapping relations. In:\nProceedings of the 49th annual meeting of the association for computa-\ntional linguistics: human language technologies; 2011. p. 541-50.\n[59] Gao J, Zhao H, Yu C, Xu R. Exploring the feasibility of ChatGPT for\nevent extraction. arXiv preprint arXiv:230303836. 2023.\n[60] Lu Y, Lin H, Xu J, Han X, Tang J, Li A, et al. Text2event: Controllable\nsequence-to-structure generation for end-to-end event extraction. arXiv\npreprint arXiv:210609232. 2021.\n31","producer":"pdfTeX-1.40.21","subject":"","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":"","trapped":"/False","page_label":"31","total_pages":35,"moddate":"2023-04-05T00:33:07+00:00","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","page":30,"keywords":"","creator":"LaTeX with hyperref","title":""}


created_at: 2026-05-29 09:32:40
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
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created_at: 2026-05-29 09:32:40
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
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created_at: 2026-05-29 09:32:40
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: e594d2e6-a48e-4b10-a307-9853ffbb2581
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{"title":"","trapped":"/False","keywords":"","chroma:document":"sushita H, Menezes A. To ship or not to ship: An extensive eval-\nuation of automatic metrics for machine translation. arXiv preprint\narXiv:210710821. 2021.\n[71] Freitag M, Rei R, Mathur N, Lo Ck, Stewart C, Avramidis E, et al.\nResults of WMT22 metrics shared task: Stop using BLEU–neural metrics\nare better and more robust. In: Proceedings of the Seventh Conference\non Machine Translation (WMT); 2022. p. 46-68.\n[72] Wang J, Liang Y, Meng F, Shi H, Li Z, Xu J, et al. Is chatgpt a good nlg\nevaluator? a preliminary study. arXiv preprint arXiv:230304048. 2023.\n32","creationdate":"2023-04-05T00:33:07+00:00","subject":"","moddate":"2023-04-05T00:33:07+00:00","page":31,"producer":"pdfTeX-1.40.21","author":"","total_pages":35,"creator":"LaTeX with hyperref","page_label":"32","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: 2026-05-29 09:32:40
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: 2dbca429-2adc-44bf-b248-5b2a2d830c44
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{"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","subject":"","author":"","chroma:document":"[73] Hermann KM, Kocisky T, Grefenstette E, Espeholt L, Kay W, Suleyman\nM, et al. Teaching machines to read and comprehend. Advances in neural\ninformation processing systems. 2015;28.\n[74] Zar JH. Spearman rank correlation. Encyclopedia of biostatistics. 2005;7.\n[75] Mukaka MM. A guide to appropriate use of correlation coeﬃcient in\nmedical research. Malawi medical journal. 2012;24(3):69-71.\n[76] Kendall MG. A new measure of rank correlation. Biometrika.\n1938;30(1/2):81-93.\n[77] Dai H, Liu Z, Liao W, Huang X, Wu Z, Zhao L, et al. ChatAug: Leveraging\nChatGPT for Text Data Augmentation. arXiv preprint arXiv:230213007.\n2023.\n[78] Wu C, Yin S, Qi W, Wang X, Tang Z, Duan N. Visual chatgpt: Talk-\ning, drawing and editing with visual foundation models. arXiv preprint\narXiv:230304671. 2023.\n[79] Zheng O, Abdel-Aty M, Wang D, Wang Z, Ding S. ChatGPT is on the\nhorizon: Could a large language model be all we need for Intelligent Trans-\nportation? arXiv preprint arXiv:230305382. 2023.","page_label":"33","page":32,"title":"","creator":"LaTeX with hyperref","total_pages":35,"producer":"pdfTeX-1.40.21","trapped":"/False","keywords":"","moddate":"2023-04-05T00:33:07+00:00","creationdate":"2023-04-05T00:33:07+00:00"}


created_at: 2026-05-29 09:32:40
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: ac86ff95-ef0f-4dce-a294-f9094e8ababc
encoding: FLOAT32
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{"creationdate":"2023-04-05T00:33:07+00:00","total_pages":35,"producer":"pdfTeX-1.40.21","page":32,"page_label":"33","keywords":"","author":"","title":"","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","moddate":"2023-04-05T00:33:07+00:00","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","trapped":"/False","chroma:document":"portation? arXiv preprint arXiv:230305382. 2023.\n[80] White J, Fu Q, Hays S, Sandborn M, Olea C, Gilbert H, et al. A prompt\npattern catalog to enhance prompt engineering with chatgpt. arXiv\npreprint arXiv:230211382. 2023.\n[81] Ahmad A, Waseem M, Liang P, Fehmideh M, Aktar MS, Mikkonen T.\nTowards Human-Bot Collaborative Software Architecting with ChatGPT.\narXiv preprint arXiv:230214600. 2023.\n[82] Lanzi PL, Loiacono D. ChatGPT and Other Large Language Models as\nEvolutionary Engines for Online Interactive Collaborative Game Design.\narXiv preprint arXiv:230302155. 2023.\n[83] Wang S, Zhao Z, Ouyang X, Wang Q, Shen D. Chatcad: Interactive\ncomputer-aided diagnosis on medical image using large language models.\narXiv preprint arXiv:230207257. 2023.\n[84] Hartmann J, Schwenzow J, Witte M. The political ideology of conver-\nsational AI: Converging evidence on ChatGPT’s pro-environmental, left-\nlibertarian orientation. arXiv preprint arXiv:230101768. 2023."}


created_at: 2026-05-29 09:32:40
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
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{"page_label":"33","chroma:document":"libertarian orientation. arXiv preprint arXiv:230101768. 2023.\n[85] Kr¨ ugel S, Ostermaier A, Uhl M. The moral authority of ChatGPT. arXiv\npreprint arXiv:230107098. 2023.\n[86] Borji A. A categorical archive of chatgpt failures. arXiv preprint\narXiv:230203494. 2023.\n33","producer":"pdfTeX-1.40.21","title":"","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","total_pages":35,"creationdate":"2023-04-05T00:33:07+00:00","subject":"","keywords":"","page":32,"trapped":"/False","moddate":"2023-04-05T00:33:07+00:00","author":""}


created_at: 2026-05-29 09:32:40
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: 6fc2bbc4-7c1b-492a-bab1-cfcf467e95ef
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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","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","creator":"LaTeX with hyperref","chroma:document":"[87] Hacker P, Engel A, Mauer M. Regulating chatgpt and other large gener-\native ai models. arXiv preprint arXiv:230202337. 2023.\n[88] Hacker P. The European AI Liability Directives–Critique of a Half-\nHearted Approach and Lessons for the Future. arXiv preprint\narXiv:221113960. 2022.\n[89] Kirk HR, Vidgen B, R¨ ottger P, Hale SA. Personalisation within bounds:\nA risk taxonomy and policy framework for the alignment of large language\nmodels with personalised feedback. arXiv preprint arXiv:230305453. 2023.\n[90] Bang Y, Cahyawijaya S, Lee N, Dai W, Su D, Wilie B, et al. A multitask,\nmultilingual, multimodal evaluation of chatgpt on reasoning, hallucina-\ntion, and interactivity. arXiv preprint arXiv:230204023. 2023.\n[91] Koco´ n J, Cichecki I, Kaszyca O, Kochanek M, Szyd lo D, Baran J,\net al. ChatGPT: Jack of all trades, master of none. arXiv preprint\narXiv:230210724. 2023.\n[92] Qin C, Zhang A, Zhang Z, Chen J, Yasunaga M, Yang D. Is chatgpt a","keywords":"","title":"","page_label":"34","producer":"pdfTeX-1.40.21","page":33,"trapped":"/False","moddate":"2023-04-05T00:33:07+00:00","creationdate":"2023-04-05T00:33:07+00:00","author":"","subject":""}


created_at: 2026-05-29 09:32:40
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: 3c014b42-c0e1-4fa9-a664-bdf75f2c6c4a
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created_at: 2026-05-29 09:32:40
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
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created_at: 2026-05-29 09:32:40
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: 6ac46876-1013-4b9a-9bdf-e968c6fa1245
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{"creator":"LaTeX with hyperref","keywords":"","moddate":"2023-04-05T00:33:07+00:00","title":"","producer":"pdfTeX-1.40.21","chroma:document":"[100] Subhash V. Can Large Language Models Change User Preference Adver-\nsarially? arXiv preprint arXiv:230210291. 2023.\n[101] Zhao L, Zhang L, Wu Z, Chen Y, Dai H, Yu X, et al. When Brain-inspired\nAI Meets AGI. arXiv preprint arXiv:230315935. 2023.\n[102] Liu Z, Yu X, Zhang L, Wu Z, Cao C, Dai H, et al. DeID-GPT:\nZero-shot Medical Text De-Identiﬁcation by GPT-4. arXiv preprint\narXiv:230311032. 2023.\n[103] Liu D, Chen Y, Wu Z. Digital Twin (DT)-CycleGAN: Enabling Zero-\nShot Sim-to-Real Transfer of Visual Grasping Models. IEEE Robotics\nand Automation Letters. 2023.\n35","author":"","total_pages":35,"creationdate":"2023-04-05T00:33:07+00:00","page_label":"35","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","page":34,"trapped":"/False","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","subject":""}


created_at: 2026-05-29 09:32:49
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: 8a2487ef-83f6-422c-aa39-3b200e79f035
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{"keywords":"","producer":"pdfTeX-1.40.21","trapped":"/False","title":"","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","creator":"LaTeX with hyperref","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","page_label":"1","total_pages":35,"author":"","moddate":"2023-04-05T00:33:07+00:00","creationdate":"2023-04-05T00:33:07+00:00","page":0,"ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2"}


created_at: 2026-05-29 09:32:49
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: 8628b361-6ee9-4671-950c-1b264f4af1f0
encoding: FLOAT32
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{"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","total_pages":35,"keywords":"","creator":"LaTeX with hyperref","author":"","creationdate":"2023-04-05T00:33:07+00:00","producer":"pdfTeX-1.40.21","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","subject":"","title":"","page":0,"page_label":"1","moddate":"2023-04-05T00:33:07+00:00"}


created_at: 2026-05-29 09:32:49
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: be4159d8-20c0-4618-b6de-8b99f86d7290
encoding: FLOAT32
metadata:
{"title":"","total_pages":35,"creator":"LaTeX with hyperref","subject":"","trapped":"/False","page":0,"page_label":"1","producer":"pdfTeX-1.40.21","keywords":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","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","author":"","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"}


created_at: 2026-05-29 09:32:49
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: 3db3153d-a4bc-49a3-bcdf-67f8c5abf564
encoding: FLOAT32
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{"creationdate":"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","author":"","title":"","subject":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","producer":"pdfTeX-1.40.21","creator":"LaTeX with hyperref","total_pages":35,"trapped":"/False","page_label":"2","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","keywords":"","page":1}


created_at: 2026-05-29 09:32:49
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: 35cb09df-c9e6-466e-9840-fff34eb1afb9
encoding: FLOAT32
metadata:
{"producer":"pdfTeX-1.40.21","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","title":"","page_label":"2","creationdate":"2023-04-05T00:33:07+00:00","total_pages":35,"keywords":"","author":"","moddate":"2023-04-05T00:33:07+00:00","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-","subject":"","page":1,"trapped":"/False"}


created_at: 2026-05-29 09:32:49
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: 7dee5f57-bc65-4a1b-a1e5-48c5f1fcf8cb
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metadata:
{"keywords":"","author":"","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","trapped":"/False","creationdate":"2023-04-05T00:33:07+00:00","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","subject":"","producer":"pdfTeX-1.40.21","page":1,"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","moddate":"2023-04-05T00:33:07+00:00","title":"","total_pages":35,"page_label":"2"}


created_at: 2026-05-29 09:32:49
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: 9922580f-4faa-4d42-badc-2882a8939e8e
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{"author":"","trapped":"/False","moddate":"2023-04-05T00:33:07+00:00","subject":"","page":1,"page_label":"2","producer":"pdfTeX-1.40.21","creator":"LaTeX with hyperref","keywords":"","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","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","creationdate":"2023-04-05T00:33:07+00:00"}


created_at: 2026-05-29 09:32:49
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: a5a26f1f-d40e-462c-8d39-848edc61ff56
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{"page_label":"3","chroma:document":"Figure 1: The number of papers submitted by researchers per day.\n3","producer":"pdfTeX-1.40.21","trapped":"/False","creator":"LaTeX with hyperref","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","creationdate":"2023-04-05T00:33:07+00:00","title":"","author":"","keywords":"","total_pages":35,"page":2,"subject":""}


created_at: 2026-05-29 09:32:49
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: 1bd90911-11ac-4510-868f-4e9e209d8f28
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{"creationdate":"2023-04-05T00:33:07+00:00","subject":"","moddate":"2023-04-05T00:33:07+00:00","author":"","page":3,"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","keywords":"","creator":"LaTeX with hyperref","page_label":"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","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","trapped":"/False","title":""}


created_at: 2026-05-29 09:32:49
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: 5c47f4d5-b786-4bac-a2a0-6954ebdbabbd
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","page":3,"keywords":"","producer":"pdfTeX-1.40.21","author":"","title":"","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","creationdate":"2023-04-05T00:33:07+00:00","subject":"","trapped":"/False","moddate":"2023-04-05T00:33:07+00:00","creator":"LaTeX with hyperref","page_label":"4","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","total_pages":35}


created_at: 2026-05-29 09:32:49
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: 84c5f4b6-1666-4faa-b89c-01c87964e349
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","moddate":"2023-04-05T00:33:07+00:00","keywords":"","total_pages":35,"page_label":"5","chroma:document":"Figure 3: The distribution of submitted papers across various ﬁelds.\n5","subject":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","trapped":"/False","page":4,"title":"","producer":"pdfTeX-1.40.21","creationdate":"2023-04-05T00:33:07+00:00","creator":"LaTeX with hyperref"}


created_at: 2026-05-29 09:32:49
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: d6385087-4d93-48b2-9cbd-7901b1088677
encoding: FLOAT32
metadata:
{"subject":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","producer":"pdfTeX-1.40.21","title":"","moddate":"2023-04-05T00:33:07+00:00","creationdate":"2023-04-05T00:33:07+00:00","page_label":"6","trapped":"/False","keywords":"","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-","page":5,"total_pages":35,"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":""}


created_at: 2026-05-29 09:32:49
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: 38a64ce8-9007-4de3-9b42-e4082931d145
encoding: FLOAT32
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{"total_pages":35,"page":5,"keywords":"","title":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","creator":"LaTeX with hyperref","author":"","trapped":"/False","subject":"","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-","page_label":"6","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","creationdate":"2023-04-05T00:33:07+00:00","producer":"pdfTeX-1.40.21"}


created_at: 2026-05-29 09:32:49
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: 77b0e9c7-a37c-4715-9e13-ef0f2caa527f
encoding: FLOAT32
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{"keywords":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","moddate":"2023-04-05T00:33:07+00:00","author":"","page_label":"6","trapped":"/False","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","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":"","creator":"LaTeX with hyperref","page":5,"subject":"","total_pages":35,"producer":"pdfTeX-1.40.21"}


created_at: 2026-05-29 09:32:49
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: 2a3cf04b-9ceb-44aa-9d28-37086059ffe1
encoding: FLOAT32
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{"trapped":"/False","subject":"","creationdate":"2023-04-05T00:33:07+00:00","page_label":"6","creator":"LaTeX with hyperref","moddate":"2023-04-05T00:33:07+00:00","keywords":"","author":"","title":"","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","producer":"pdfTeX-1.40.21","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","page":5}


created_at: 2026-05-29 09:32:49
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: fe5dfe4d-966c-4156-be6a-41a880ddb722
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{"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","author":"","moddate":"2023-04-05T00:33:07+00:00","creator":"LaTeX with hyperref","total_pages":35,"creationdate":"2023-04-05T00:33:07+00:00","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","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":"","trapped":"/False","page":6,"title":"","subject":"","page_label":"7"}


created_at: 2026-05-29 09:32:49
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: eb91195f-bde8-4f59-8ed7-c437eb292bfe
encoding: FLOAT32
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{"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,"subject":"","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","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","title":"","keywords":"","page":6,"producer":"pdfTeX-1.40.21","creationdate":"2023-04-05T00:33:07+00:00","creator":"LaTeX with hyperref","author":"","moddate":"2023-04-05T00:33:07+00:00"}


created_at: 2026-05-29 09:32:49
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: 4bb2f857-7ee7-4418-9278-63d4e5640be5
encoding: FLOAT32
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{"creator":"LaTeX with hyperref","page":6,"total_pages":35,"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","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","keywords":"","title":"","subject":"","creationdate":"2023-04-05T00:33:07+00:00","trapped":"/False","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","page_label":"7","author":"","producer":"pdfTeX-1.40.21","moddate":"2023-04-05T00:33:07+00:00"}


created_at: 2026-05-29 09:32:49
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: 3b38c2e7-262a-45e9-8291-a66b2ba29862
encoding: FLOAT32
metadata:
{"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","title":"","keywords":"","subject":"","page":6,"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","creationdate":"2023-04-05T00:33:07+00:00","moddate":"2023-04-05T00:33:07+00:00","creator":"LaTeX with hyperref","page_label":"7","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","trapped":"/False","total_pages":35}


created_at: 2026-05-29 09:32:49
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: b2ccd963-0c59-4da4-8f68-9e767e0832cb
encoding: FLOAT32
metadata:
{"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","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","page":7,"author":"","page_label":"8","creator":"LaTeX with hyperref","title":"","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","subject":"","total_pages":35,"creationdate":"2023-04-05T00:33:07+00:00","moddate":"2023-04-05T00:33:07+00:00","keywords":""}


created_at: 2026-05-29 09:32:49
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: 05ee58d3-03bb-4660-ae57-92b5db27534b
encoding: FLOAT32
metadata:
{"page_label":"8","page":7,"author":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","title":"","creator":"LaTeX with hyperref","subject":"","total_pages":35,"keywords":"","trapped":"/False","moddate":"2023-04-05T00:33:07+00:00","producer":"pdfTeX-1.40.21","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","creationdate":"2023-04-05T00:33:07+00:00","source":"./data/Summary of ChatGPTGPT-4 Research.pdf"}


created_at: 2026-05-29 09:32:49
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: 0cf7b2c8-45f4-4acf-9844-60e54126083d
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":"","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-","creationdate":"2023-04-05T00:33:07+00:00","author":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","page":7,"subject":"","trapped":"/False","page_label":"8","title":"","total_pages":35,"creator":"LaTeX with hyperref","producer":"pdfTeX-1.40.21","moddate":"2023-04-05T00:33:07+00:00"}


created_at: 2026-05-29 09:32:49
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: 1fee5990-5f5e-4ad1-a98b-d9b4b40414b6
encoding: FLOAT32
metadata:
{"trapped":"/False","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","total_pages":35,"moddate":"2023-04-05T00:33:07+00:00","page_label":"8","creator":"LaTeX with hyperref","subject":"","creationdate":"2023-04-05T00:33:07+00:00","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,"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":"","author":""}


created_at: 2026-05-29 09:32:49
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: a4e3cda2-b0f5-4a4e-a4c7-0a1c0a359083
encoding: FLOAT32
metadata:
{"total_pages":35,"page":8,"page_label":"9","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","creationdate":"2023-04-05T00:33:07+00:00","keywords":"","author":"","producer":"pdfTeX-1.40.21","title":"","subject":"","trapped":"/False","moddate":"2023-04-05T00:33:07+00:00","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"}


created_at: 2026-05-29 09:32:49
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: 488b6496-b39e-4c7c-9128-742138d1fa12
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{"page_label":"9","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","trapped":"/False","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","title":"","creator":"LaTeX with hyperref","total_pages":35,"producer":"pdfTeX-1.40.21","keywords":"","page":8,"subject":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","author":"","creationdate":"2023-04-05T00:33:07+00:00","moddate":"2023-04-05T00:33:07+00:00"}


created_at: 2026-05-29 09:32:49
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: 095fd4cb-db2a-44f3-abf5-8a465f103536
encoding: FLOAT32
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{"subject":"","creationdate":"2023-04-05T00:33:07+00:00","creator":"LaTeX with hyperref","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","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":"9","title":"","keywords":"","author":"","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:","trapped":"/False","producer":"pdfTeX-1.40.21","moddate":"2023-04-05T00:33:07+00:00","page":8}


created_at: 2026-05-29 09:32:49
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: 253d6e72-c1df-48ae-8fe1-29d1f0c1457c
encoding: FLOAT32
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{"producer":"pdfTeX-1.40.21","title":"","author":"","subject":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","keywords":"","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","page":8,"total_pages":35,"page_label":"9","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","creator":"LaTeX with hyperref","trapped":"/False"}


created_at: 2026-05-29 09:32:49
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: 51f4518e-552a-4edf-bac5-17825e66cb06
encoding: FLOAT32
metadata:
{"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","moddate":"2023-04-05T00:33:07+00:00","total_pages":35,"keywords":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","author":"","producer":"pdfTeX-1.40.21","trapped":"/False","title":"","subject":"","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","page":9,"page_label":"10","creationdate":"2023-04-05T00:33:07+00:00"}


created_at: 2026-05-29 09:32:49
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: 2a722dc7-539d-42ac-9d43-4637213b799b
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{"subject":"","page":9,"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","producer":"pdfTeX-1.40.21","author":"","keywords":"","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","total_pages":35,"trapped":"/False","page_label":"10","creator":"LaTeX with hyperref","creationdate":"2023-04-05T00:33:07+00:00","moddate":"2023-04-05T00:33:07+00:00"}


created_at: 2026-05-29 09:32:49
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: b0b131b1-2f23-4645-8973-65bd031970f3
encoding: FLOAT32
metadata:
{"moddate":"2023-04-05T00:33:07+00:00","producer":"pdfTeX-1.40.21","creationdate":"2023-04-05T00:33:07+00:00","total_pages":35,"author":"","subject":"","creator":"LaTeX with hyperref","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,"ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","keywords":"","trapped":"/False","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","title":"","page_label":"10"}


created_at: 2026-05-29 09:32:49
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: 1eef8fed-e3a9-4a33-ac0f-fe823f803f7f
encoding: FLOAT32
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{"total_pages":35,"page":9,"trapped":"/False","keywords":"","title":"","page_label":"10","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","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","author":"","creator":"LaTeX with hyperref","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","producer":"pdfTeX-1.40.21","creationdate":"2023-04-05T00:33:07+00:00"}


created_at: 2026-05-29 09:32:49
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: af22be08-0dfa-4440-9f4a-361cc2b7cc16
encoding: FLOAT32
metadata:
{"creationdate":"2023-04-05T00:33:07+00:00","producer":"pdfTeX-1.40.21","total_pages":35,"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","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","trapped":"/False","moddate":"2023-04-05T00:33:07+00:00","title":"","subject":"","page":10,"author":"","page_label":"11","creator":"LaTeX with hyperref"}


created_at: 2026-05-29 09:32:49
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: 4d9cdd67-c2b6-40a0-9017-976273942398
encoding: FLOAT32
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{"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":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","creationdate":"2023-04-05T00:33:07+00:00","title":"","total_pages":35,"producer":"pdfTeX-1.40.21","creator":"LaTeX with hyperref","subject":"","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","trapped":"/False","page_label":"11","page":10}


created_at: 2026-05-29 09:32:49
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: 70098d03-6091-48fd-b2af-7568fed2bb4f
encoding: FLOAT32
metadata:
{"author":"","page":10,"title":"","page_label":"11","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","total_pages":35,"subject":"","moddate":"2023-04-05T00:33:07+00:00","trapped":"/False","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","producer":"pdfTeX-1.40.21","creator":"LaTeX with hyperref","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","keywords":""}


created_at: 2026-05-29 09:32:49
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: 6174a9a1-8b41-4b65-b36c-b942f73e4334
encoding: FLOAT32
metadata:
{"title":"","keywords":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","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","page":10,"total_pages":35,"producer":"pdfTeX-1.40.21","subject":"","page_label":"11","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","moddate":"2023-04-05T00:33:07+00:00","creator":"LaTeX with hyperref"}


created_at: 2026-05-29 09:32:49
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: a1caa7ae-df96-4fd2-8c39-128257c96ad0
encoding: FLOAT32
metadata:
{"title":"","author":"","keywords":"","creationdate":"2023-04-05T00:33:07+00:00","total_pages":35,"creator":"LaTeX with hyperref","subject":"","page":11,"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","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","page_label":"12","moddate":"2023-04-05T00:33:07+00:00","trapped":"/False","producer":"pdfTeX-1.40.21"}


created_at: 2026-05-29 09:32:49
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: 4c5746f5-7610-4bdd-a18a-ea1e7b6a009d
encoding: FLOAT32
metadata:
{"total_pages":35,"title":"","producer":"pdfTeX-1.40.21","trapped":"/False","author":"","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":11,"creationdate":"2023-04-05T00:33:07+00:00","page_label":"12","keywords":"","subject":"","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"}


created_at: 2026-05-29 09:32:49
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: 40f773ea-8c37-43fa-996e-82ad8d07626f
encoding: FLOAT32
metadata:
{"keywords":"","trapped":"/False","title":"","page":11,"subject":"","creator":"LaTeX with hyperref","author":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","page_label":"12","total_pages":35,"creationdate":"2023-04-05T00:33:07+00:00","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","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","moddate":"2023-04-05T00:33:07+00:00"}


created_at: 2026-05-29 09:32:49
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: d0463b0b-0136-4396-b3f2-fde387e1a025
encoding: FLOAT32
metadata:
{"title":"","producer":"pdfTeX-1.40.21","total_pages":35,"author":"","subject":"","trapped":"/False","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","keywords":"","creationdate":"2023-04-05T00:33:07+00:00","moddate":"2023-04-05T00:33:07+00:00","creator":"LaTeX with hyperref","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","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","page":11,"page_label":"12"}


created_at: 2026-05-29 09:32:49
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: 0b7d98e3-518b-4270-a7fa-710eceb12759
encoding: FLOAT32
metadata:
{"source":"./data/Summary of ChatGPTGPT-4 Research.pdf","creationdate":"2023-04-05T00:33:07+00:00","total_pages":35,"creator":"LaTeX with hyperref","subject":"","page":12,"title":"","author":"","keywords":"","producer":"pdfTeX-1.40.21","trapped":"/False","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":"13","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"}


created_at: 2026-05-29 09:32:49
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: 64681cee-0f98-46ab-aad1-5f85093cb059
encoding: FLOAT32
metadata:
{"author":"","total_pages":35,"creationdate":"2023-04-05T00:33:07+00:00","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-","title":"","page_label":"13","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","keywords":"","producer":"pdfTeX-1.40.21","page":12,"subject":"","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","moddate":"2023-04-05T00:33:07+00:00"}


created_at: 2026-05-29 09:32:49
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: fde28175-d93b-4dc6-a10f-432350cac2c7
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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","page_label":"13","moddate":"2023-04-05T00:33:07+00:00","producer":"pdfTeX-1.40.21","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","title":"","keywords":"","trapped":"/False","author":"","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","total_pages":35,"creationdate":"2023-04-05T00:33:07+00:00","creator":"LaTeX with hyperref","page":12}


created_at: 2026-05-29 09:32:49
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
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{"trapped":"/False","creator":"LaTeX with hyperref","keywords":"","page":12,"subject":"","total_pages":35,"creationdate":"2023-04-05T00:33:07+00:00","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","producer":"pdfTeX-1.40.21","title":"","page_label":"13","moddate":"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","author":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2"}


created_at: 2026-05-29 09:32:49
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: 000f9509-9b69-4187-b209-a1c2bb6790fe
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{"page_label":"14","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","total_pages":35,"trapped":"/False","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","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","keywords":"","subject":"","producer":"pdfTeX-1.40.21","page":13,"creator":"LaTeX with hyperref","title":"","creationdate":"2023-04-05T00:33:07+00:00","author":"","moddate":"2023-04-05T00:33:07+00:00"}


created_at: 2026-05-29 09:32:49
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: 81547e29-f578-443f-a837-98cf7c58dad1
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{"author":"","trapped":"/False","subject":"","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","moddate":"2023-04-05T00:33:07+00:00","title":"","creator":"LaTeX with hyperref","creationdate":"2023-04-05T00:33:07+00:00","page_label":"14","page":13,"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","keywords":"","total_pages":35}


created_at: 2026-05-29 09:32:49
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: e04686f5-9c3f-4b23-8886-75ba58b54def
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{"trapped":"/False","title":"","keywords":"","producer":"pdfTeX-1.40.21","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","subject":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","author":"","page_label":"14","creator":"LaTeX with hyperref","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","moddate":"2023-04-05T00:33:07+00:00","page":13,"total_pages":35}


created_at: 2026-05-29 09:32:49
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: 2612565f-846c-4619-bac7-73b1e0fc0d31
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{"producer":"pdfTeX-1.40.21","title":"","trapped":"/False","page_label":"14","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":13,"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","subject":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","author":"","keywords":"","creator":"LaTeX with hyperref","total_pages":35,"creationdate":"2023-04-05T00:33:07+00:00"}


created_at: 2026-05-29 09:32:49
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: a5cd3b1d-67d5-474a-bf6c-da568c5f27aa
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{"keywords":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","page":14,"title":"","trapped":"/False","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":"","page_label":"15","moddate":"2023-04-05T00:33:07+00:00","total_pages":35,"producer":"pdfTeX-1.40.21","subject":"","creator":"LaTeX with hyperref","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: 2026-05-29 09:32:49
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: c3673b15-6e64-4592-bdbb-9f04a13599c1
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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","subject":"","creationdate":"2023-04-05T00:33:07+00:00","title":"","creator":"LaTeX with hyperref","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","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","page_label":"15","moddate":"2023-04-05T00:33:07+00:00","producer":"pdfTeX-1.40.21","author":"","keywords":"","page":14,"total_pages":35,"trapped":"/False"}


created_at: 2026-05-29 09:32:49
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: 937073f2-80b6-408b-8e0e-eaaf96d5703b
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{"subject":"","creationdate":"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-","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":14,"source":"./data/Summary of ChatGPTGPT-4 Research.pdf","title":"","creator":"LaTeX with hyperref","trapped":"/False","moddate":"2023-04-05T00:33:07+00:00","page_label":"15","author":""}


created_at: 2026-05-29 09:32:49
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: 33797976-711c-4945-ad0b-20efbc56a263
encoding: FLOAT32
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{"moddate":"2023-04-05T00:33:07+00:00","producer":"pdfTeX-1.40.21","page_label":"15","creationdate":"2023-04-05T00:33:07+00:00","subject":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","trapped":"/False","creator":"LaTeX with hyperref","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","title":"","total_pages":35,"keywords":"","page":14,"source":"./data/Summary of ChatGPTGPT-4 Research.pdf"}


created_at: 2026-05-29 09:32:49
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: 585b30d3-4dd4-4715-a543-323c62f46c8a
encoding: FLOAT32
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{"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","producer":"pdfTeX-1.40.21","title":"","author":"","page_label":"16","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","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-","subject":"","page":15,"creationdate":"2023-04-05T00:33:07+00:00","keywords":"","creator":"LaTeX with hyperref","trapped":"/False"}


created_at: 2026-05-29 09:32:49
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: 8df67eff-e86b-484e-921d-47c7f0fad7b5
encoding: FLOAT32
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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,"trapped":"/False","author":"","creationdate":"2023-04-05T00:33:07+00:00","keywords":"","title":"","page":15,"moddate":"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","subject":"","producer":"pdfTeX-1.40.21","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","page_label":"16"}


created_at: 2026-05-29 09:32:49
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: 397bc02f-a3b6-41f7-9790-2b5731c1cd49
encoding: FLOAT32
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{"subject":"","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","trapped":"/False","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","creator":"LaTeX with hyperref","page":15,"keywords":"","moddate":"2023-04-05T00:33:07+00:00","page_label":"16","title":"","total_pages":35,"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","author":""}


created_at: 2026-05-29 09:32:49
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: 3f34d6c3-d476-429a-8e86-e378fe6dcf70
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{"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","trapped":"/False","page":15,"producer":"pdfTeX-1.40.21","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","creationdate":"2023-04-05T00:33:07+00:00","creator":"LaTeX with hyperref","total_pages":35,"moddate":"2023-04-05T00:33:07+00:00","title":"","keywords":"","page_label":"16","subject":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","author":""}


created_at: 2026-05-29 09:32:49
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: 888fd099-9769-498d-a56c-145b7e1862ad
encoding: FLOAT32
metadata:
{"source":"./data/Summary of ChatGPTGPT-4 Research.pdf","author":"","producer":"pdfTeX-1.40.21","total_pages":35,"creator":"LaTeX with hyperref","page":16,"page_label":"17","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":"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","creationdate":"2023-04-05T00:33:07+00:00","keywords":"","moddate":"2023-04-05T00:33:07+00:00","title":""}


created_at: 2026-05-29 09:32:49
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: 91f99d9d-917d-4ee0-8f6d-3f43bd84f27d
encoding: FLOAT32
metadata:
{"author":"","subject":"","trapped":"/False","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","keywords":"","creator":"LaTeX with hyperref","page_label":"17","producer":"pdfTeX-1.40.21","title":"","total_pages":35,"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","moddate":"2023-04-05T00:33:07+00:00","page":16}


created_at: 2026-05-29 09:32:49
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: ce3a2443-d623-47ee-8f25-f89128d71de8
encoding: FLOAT32
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{"total_pages":35,"producer":"pdfTeX-1.40.21","creator":"LaTeX with hyperref","keywords":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","page":16,"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,","subject":"","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","page_label":"17","trapped":"/False"}


created_at: 2026-05-29 09:32:49
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: 8e6b3185-02b7-4510-9290-6e2d00d9a751
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{"producer":"pdfTeX-1.40.21","title":"","page":16,"page_label":"17","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","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","trapped":"/False","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","author":"","keywords":"","total_pages":35,"subject":"","creationdate":"2023-04-05T00:33:07+00:00"}


created_at: 2026-05-29 09:32:49
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: 1256f8d7-e16e-45ee-8ff8-e46541a618f4
encoding: FLOAT32
metadata:
{"total_pages":35,"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","trapped":"/False","author":"","keywords":"","producer":"pdfTeX-1.40.21","page_label":"18","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","page":17,"creationdate":"2023-04-05T00:33:07+00:00","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","subject":"","title":"","moddate":"2023-04-05T00:33:07+00:00"}


created_at: 2026-05-29 09:32:49
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: f13db929-72ef-40ae-8385-9634201e7514
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{"page":17,"creationdate":"2023-04-05T00:33:07+00:00","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","keywords":"","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","author":"","trapped":"/False","moddate":"2023-04-05T00:33:07+00:00","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","subject":"","page_label":"18","creator":"LaTeX with hyperref","total_pages":35}


created_at: 2026-05-29 09:32:49
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: ca3f22fd-f236-4a20-9f8b-8f2b71721f0c
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metadata:
{"title":"","keywords":"","subject":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","page_label":"18","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","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.","total_pages":35,"creator":"LaTeX with hyperref","creationdate":"2023-04-05T00:33:07+00:00","producer":"pdfTeX-1.40.21","trapped":"/False","moddate":"2023-04-05T00:33:07+00:00","author":"","page":17}


created_at: 2026-05-29 09:32:49
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: c979ec81-5c8a-4371-8027-07b8bf99026e
encoding: FLOAT32
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{"page":17,"trapped":"/False","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","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","moddate":"2023-04-05T00:33:07+00:00","title":"","keywords":"","subject":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","creationdate":"2023-04-05T00:33:07+00:00","creator":"LaTeX with hyperref","author":"","page_label":"18"}


created_at: 2026-05-29 09:32:49
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: 782a2797-f661-43a9-9ad4-e2818fef7c3d
encoding: FLOAT32
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{"keywords":"","creator":"LaTeX with hyperref","title":"","total_pages":35,"author":"","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","trapped":"/False","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","page":18,"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":"19","moddate":"2023-04-05T00:33:07+00:00","subject":""}


created_at: 2026-05-29 09:32:49
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: f664408f-d3fd-4eb9-a731-23b15f030e0b
encoding: FLOAT32
metadata:
{"ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","page_label":"19","total_pages":35,"subject":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","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-","title":"","creator":"LaTeX with hyperref","producer":"pdfTeX-1.40.21","creationdate":"2023-04-05T00:33:07+00:00","author":"","moddate":"2023-04-05T00:33:07+00:00","trapped":"/False","page":18,"keywords":""}


created_at: 2026-05-29 09:32:49
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: 68379082-f2c9-4704-865b-791a342e8de6
encoding: FLOAT32
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{"title":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","moddate":"2023-04-05T00:33:07+00:00","trapped":"/False","total_pages":35,"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","producer":"pdfTeX-1.40.21","creator":"LaTeX with hyperref","author":"","subject":"","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","page_label":"19","keywords":"","page":18}


created_at: 2026-05-29 09:32:49
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: e9915249-032e-47f6-85a6-9d673ead5abf
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{"trapped":"/False","keywords":"","title":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","creationdate":"2023-04-05T00:33:07+00:00","moddate":"2023-04-05T00:33:07+00:00","subject":"","producer":"pdfTeX-1.40.21","page_label":"19","author":"","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","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":18}


created_at: 2026-05-29 09:32:49
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: 09c8199b-5220-40b7-9a01-bedaacf73f15
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{"page_label":"20","creationdate":"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-","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":19,"author":"","creator":"LaTeX with hyperref","keywords":"","trapped":"/False","moddate":"2023-04-05T00:33:07+00:00","title":"","producer":"pdfTeX-1.40.21","subject":"","total_pages":35}


created_at: 2026-05-29 09:32:49
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: 18b5c534-43f1-41de-9bd9-c29c5eb15d45
encoding: FLOAT32
metadata:
{"creationdate":"2023-04-05T00:33:07+00:00","producer":"pdfTeX-1.40.21","page":19,"author":"","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","moddate":"2023-04-05T00:33:07+00:00","creator":"LaTeX with hyperref","keywords":"","page_label":"20","subject":"","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","total_pages":35}


created_at: 2026-05-29 09:32:49
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: f136abea-9d6b-42c6-87d1-a6a99b404200
encoding: FLOAT32
metadata:
{"creator":"LaTeX with hyperref","author":"","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","title":"","page_label":"20","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","subject":"","keywords":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","trapped":"/False","producer":"pdfTeX-1.40.21","page":19,"moddate":"2023-04-05T00:33:07+00:00"}


created_at: 2026-05-29 09:32:49
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: a19a7e99-7548-4d12-9a0e-55ab8fd51da2
encoding: FLOAT32
metadata:
{"title":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","trapped":"/False","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","keywords":"","page_label":"20","total_pages":35,"moddate":"2023-04-05T00:33:07+00:00","subject":"","page":19,"producer":"pdfTeX-1.40.21","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","creator":"LaTeX with hyperref","author":""}


created_at: 2026-05-29 09:32:49
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: a2ede4ac-c605-4673-823e-644fdf5a1061
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","keywords":"","total_pages":35,"producer":"pdfTeX-1.40.21","subject":"","moddate":"2023-04-05T00:33:07+00:00","author":"","title":"","page":20,"page_label":"21","creationdate":"2023-04-05T00:33:07+00:00","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.","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","creator":"LaTeX with hyperref"}


created_at: 2026-05-29 09:32:49
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: 3babd608-e5ff-43e3-8682-1a6ec0e11267
encoding: FLOAT32
metadata:
{"ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","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.","total_pages":35,"keywords":"","title":"","producer":"pdfTeX-1.40.21","creator":"LaTeX with hyperref","subject":"","author":"","creationdate":"2023-04-05T00:33:07+00:00","moddate":"2023-04-05T00:33:07+00:00","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","page_label":"21","trapped":"/False","page":20}


created_at: 2026-05-29 09:32:49
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: fa745008-b1ce-448c-b1f1-3ef1398debbb
encoding: FLOAT32
metadata:
{"trapped":"/False","producer":"pdfTeX-1.40.21","page":20,"creationdate":"2023-04-05T00:33:07+00:00","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","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,","page_label":"21","title":"","subject":"","creator":"LaTeX with hyperref","moddate":"2023-04-05T00:33:07+00:00","keywords":"","total_pages":35}


created_at: 2026-05-29 09:32:49
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: 2ecef683-87a1-496a-9ff2-2035d7443051
encoding: FLOAT32
metadata:
{"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","author":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","subject":"","creationdate":"2023-04-05T00:33:07+00:00","total_pages":35,"producer":"pdfTeX-1.40.21","title":"","trapped":"/False","keywords":"","page":20,"page_label":"21","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"}


created_at: 2026-05-29 09:32:49
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: 3cabf269-02cb-4bdf-b065-433015760063
encoding: FLOAT32
metadata:
{"source":"./data/Summary of ChatGPTGPT-4 Research.pdf","subject":"","creator":"LaTeX with hyperref","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","author":"","page":21,"moddate":"2023-04-05T00:33:07+00:00","title":"","page_label":"22","trapped":"/False","total_pages":35,"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","keywords":"","creationdate":"2023-04-05T00:33:07+00:00"}


created_at: 2026-05-29 09:32:49
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: 5fdfb576-1baf-4f42-ae53-587c29affe1f
encoding: FLOAT32
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{"keywords":"","subject":"","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","title":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","moddate":"2023-04-05T00:33:07+00:00","author":"","total_pages":35,"creationdate":"2023-04-05T00:33:07+00:00","page":21,"page_label":"22","creator":"LaTeX with hyperref","trapped":"/False","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"}


created_at: 2026-05-29 09:32:49
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: fdc67ca2-8a7c-4782-86c1-472f5f8e7755
encoding: FLOAT32
metadata:
{"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":"","page":21,"title":"","trapped":"/False","page_label":"22","keywords":"","producer":"pdfTeX-1.40.21","subject":"","creationdate":"2023-04-05T00:33:07+00:00","total_pages":35,"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","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","moddate":"2023-04-05T00:33:07+00:00"}


created_at: 2026-05-29 09:32:49
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: 08b01b1a-6f42-40c2-a170-c407e9ddc911
encoding: FLOAT32
metadata:
{"subject":"","author":"","creator":"LaTeX with hyperref","trapped":"/False","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","keywords":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","producer":"pdfTeX-1.40.21","creationdate":"2023-04-05T00:33:07+00:00","title":"","page_label":"22","page":21,"total_pages":35,"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"}


created_at: 2026-05-29 09:32:49
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: 1bb5a738-a18d-4e7c-b235-bbcd44d7bf05
encoding: FLOAT32
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{"page_label":"23","keywords":"","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","creator":"LaTeX with hyperref","author":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","trapped":"/False","moddate":"2023-04-05T00:33:07+00:00","total_pages":35,"page":22,"title":"","producer":"pdfTeX-1.40.21","subject":"","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: 2026-05-29 09:32:49
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: fe0f6874-977a-4b8d-aac9-98a0564fa5f6
encoding: FLOAT32
metadata:
{"source":"./data/Summary of ChatGPTGPT-4 Research.pdf","total_pages":35,"title":"","page":22,"page_label":"23","keywords":"","author":"","moddate":"2023-04-05T00:33:07+00:00","creator":"LaTeX with hyperref","producer":"pdfTeX-1.40.21","trapped":"/False","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","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","subject":""}


created_at: 2026-05-29 09:32:49
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: 21aefe4a-e0d4-4844-86e7-57f18b7db2cd
encoding: FLOAT32
metadata:
{"total_pages":35,"creationdate":"2023-04-05T00:33:07+00:00","keywords":"","subject":"","creator":"LaTeX with hyperref","trapped":"/False","author":"","producer":"pdfTeX-1.40.21","page":22,"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","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","page_label":"23","title":"","moddate":"2023-04-05T00:33:07+00:00"}


created_at: 2026-05-29 09:32:49
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: 61045064-20be-44f6-9ce3-45f2f9a6334e
encoding: FLOAT32
metadata:
{"trapped":"/False","page":22,"keywords":"","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","creationdate":"2023-04-05T00:33:07+00:00","moddate":"2023-04-05T00:33:07+00:00","creator":"LaTeX with hyperref","producer":"pdfTeX-1.40.21","total_pages":35,"page_label":"23","subject":"","author":"","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"}


created_at: 2026-05-29 09:32:49
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: 399043f7-f4e1-4845-bcdb-c8b97a13d7fe
encoding: FLOAT32
metadata:
{"subject":"","creator":"LaTeX with hyperref","moddate":"2023-04-05T00:33:07+00:00","author":"","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","producer":"pdfTeX-1.40.21","page":23,"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","keywords":"","trapped":"/False","page_label":"24","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","total_pages":35,"title":""}


created_at: 2026-05-29 09:32:49
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: 2e5558b3-b975-44e2-b814-a0ad8b630a6a
encoding: FLOAT32
metadata:
{"ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","page_label":"24","title":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","page":23,"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","total_pages":35,"creator":"LaTeX with hyperref","creationdate":"2023-04-05T00:33:07+00:00","producer":"pdfTeX-1.40.21","keywords":"","trapped":"/False","subject":"","author":"","moddate":"2023-04-05T00:33:07+00:00"}


created_at: 2026-05-29 09:32:49
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: 866cf323-7a83-4b91-a8e8-ea552f69f5e8
encoding: FLOAT32
metadata:
{"author":"","keywords":"","creationdate":"2023-04-05T00:33:07+00:00","page":23,"creator":"LaTeX with hyperref","page_label":"24","producer":"pdfTeX-1.40.21","moddate":"2023-04-05T00:33:07+00:00","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","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","trapped":"/False","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}


created_at: 2026-05-29 09:32:49
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: 8d18df66-530c-44d9-8eb1-bf97aba08e9f
encoding: FLOAT32
metadata:
{"creationdate":"2023-04-05T00:33:07+00:00","keywords":"","producer":"pdfTeX-1.40.21","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.","title":"","creator":"LaTeX with hyperref","total_pages":35,"source":"./data/Summary of ChatGPTGPT-4 Research.pdf","trapped":"/False","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","page":24,"author":"","page_label":"25"}


created_at: 2026-05-29 09:32:49
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: 4619a9cb-4ed4-4cea-91e5-5946970dbc85
encoding: FLOAT32
metadata:
{"page":24,"author":"","title":"","total_pages":35,"creationdate":"2023-04-05T00:33:07+00:00","producer":"pdfTeX-1.40.21","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.","trapped":"/False","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":"25","creator":"LaTeX with hyperref","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","subject":"","keywords":""}


created_at: 2026-05-29 09:32:49
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: 68882353-2e49-40bd-b90b-f9fccf0a66ce
encoding: FLOAT32
metadata:
{"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","page_label":"25","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","title":"","subject":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","keywords":"","author":"","producer":"pdfTeX-1.40.21","page":24,"total_pages":35,"moddate":"2023-04-05T00:33:07+00:00","creator":"LaTeX with hyperref"}


created_at: 2026-05-29 09:32:49
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: 9bb0d05a-709b-4a54-8210-c876b0213e38
encoding: FLOAT32
metadata:
{"creationdate":"2023-04-05T00:33:07+00:00","keywords":"","title":"","page_label":"26","trapped":"/False","page":25,"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":"","moddate":"2023-04-05T00:33:07+00:00","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.","subject":"","producer":"pdfTeX-1.40.21","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","total_pages":35}


