# migrations

dir: sysdb
filename: 00001-collections.sqlite.sql
hash: 38352d725ad1c16074fac420b22b4633
sql:
CREATE TABLE collections (
    id TEXT PRIMARY KEY,
    name TEXT NOT NULL,
    topic TEXT NOT NULL,
    UNIQUE (name)
);

CREATE TABLE collection_metadata (
    collection_id TEXT REFERENCES collections(id) ON DELETE CASCADE,
    key TEXT NOT NULL,
    str_value TEXT,
    int_value INTEGER,
    float_value REAL,
    PRIMARY KEY (collection_id, key)
);


dir: sysdb
filename: 00002-segments.sqlite.sql
hash: 2913cb6a503055a95f625448037e8912
sql:
CREATE TABLE segments (
    id TEXT PRIMARY KEY,
    type TEXT NOT NULL,
    scope TEXT NOT NULL,
    topic TEXT,
    collection TEXT REFERENCES collection(id)
);

CREATE TABLE segment_metadata (
    segment_id TEXT  REFERENCES segments(id) ON DELETE CASCADE,
    key TEXT NOT NULL,
    str_value TEXT,
    int_value INTEGER,
    float_value REAL,
    PRIMARY KEY (segment_id, key)
);


dir: sysdb
filename: 00004-tenants-databases.sqlite.sql
hash: 048867ce8fcdefe4023c7110e4433591
sql:
CREATE TABLE IF NOT EXISTS tenants (
    id TEXT PRIMARY KEY,
    UNIQUE (id)
);

CREATE TABLE IF NOT EXISTS databases (
    id TEXT PRIMARY KEY, -- unique globally
    name TEXT NOT NULL, -- unique per tenant
    tenant_id TEXT NOT NULL REFERENCES tenants(id) ON DELETE CASCADE,
    UNIQUE (tenant_id, name) -- Ensure that a tenant has only one database with a given name
);

CREATE TABLE IF NOT EXISTS collections_tmp (
    id TEXT PRIMARY KEY, -- unique globally
    name TEXT NOT NULL, -- unique per database
    topic TEXT NOT NULL,
    dimension INTEGER,
    database_id TEXT NOT NULL REFERENCES databases(id) ON DELETE CASCADE,
    UNIQUE (name, database_id)
);

-- Create default tenant and database
INSERT OR REPLACE INTO tenants (id) VALUES ('default_tenant'); -- The default tenant id is 'default_tenant' others are UUIDs
INSERT OR REPLACE INTO databases (id, name, tenant_id) VALUES ('00000000-0000-0000-0000-000000000000', 'default_database', 'default_tenant');

INSERT OR REPLACE INTO collections_tmp (id, name, topic, dimension, database_id)
    SELECT id, name, topic, dimension, '00000000-0000-0000-0000-000000000000' FROM collections;
DROP TABLE collections;
ALTER TABLE collections_tmp RENAME TO collections;


dir: sysdb
filename: 00005-remove-topic.sqlite.sql
hash: b1367c826b8fba5f96f27befdc1d42d2
sql:
-- Remove the topic column from the Collections and Segments tables

ALTER TABLE collections DROP COLUMN topic;
ALTER TABLE segments DROP COLUMN topic;


dir: sysdb
filename: 00006-collection-segment-metadata.sqlite.sql
hash: 4eea7468935bf25d4604a0fed2366116
sql:
-- SQLite does not support adding check with alter table, as a result, adding a check
-- involve creating a new table and copying the data over. It is over kill with adding
-- a boolean type column. The application write to the table needs to ensure the data
-- integrity.
ALTER TABLE collection_metadata ADD COLUMN bool_value INTEGER;
ALTER TABLE segment_metadata ADD COLUMN bool_value INTEGER;


dir: sysdb
filename: 00007-collection-config.sqlite.sql
hash: 1c7e63bba346a42a18b6ab7f1c989bed
sql:
-- Stores collection configuration dictionaries.
ALTER TABLE collections ADD COLUMN config_json_str TEXT;


dir: sysdb
filename: 00008-maintenance-log.sqlite.sql
hash: 0a0e7e93111a01789addf64961c6127c
sql:
-- Records when database maintenance operations are performed.
-- At time of creation, this table is only used to record vacuum operations.
CREATE TABLE maintenance_log (
  id INT PRIMARY KEY,
  timestamp INT NOT NULL,
  operation TEXT NOT NULL
);


dir: sysdb
filename: 00009-segment-collection-not-null.sqlite.sql
hash: 054355aef9e63702bf54ea29e61563f1
sql:
-- This makes segments.collection non-nullable.
CREATE TABLE segments_temp (
    id TEXT PRIMARY KEY,
    type TEXT NOT NULL,
    scope TEXT NOT NULL,
    collection TEXT REFERENCES collection(id) NOT NULL
);

INSERT INTO segments_temp SELECT * FROM segments;
DROP TABLE segments;
ALTER TABLE segments_temp RENAME TO segments;


dir: sysdb
filename: 00010-collection-schema.sqlite.sql
hash: 5c3a5ac4b79df76799b4721827ed5e1d
sql:
-- Stores collection schema as stringified json
ALTER TABLE collections ADD COLUMN schema_str TEXT;


dir: metadb
filename: 00001-embedding-metadata.sqlite.sql
hash: 2b4cf52c4bb2676e21d6860a4409f856
sql:
CREATE TABLE embeddings (
    id INTEGER PRIMARY KEY,
    segment_id TEXT NOT NULL,
    embedding_id TEXT NOT NULL,
    seq_id BLOB NOT NULL,
    created_at TIMESTAMP NOT NULL DEFAULT CURRENT_TIMESTAMP,
    UNIQUE (segment_id, embedding_id)
);

CREATE TABLE embedding_metadata (
    id INTEGER REFERENCES embeddings(id),
    key TEXT NOT NULL,
    string_value TEXT,
    int_value INTEGER,
    float_value REAL,
    PRIMARY KEY (id, key)
);

CREATE TABLE max_seq_id (
    segment_id TEXT PRIMARY KEY,
    seq_id BLOB NOT NULL
);

CREATE VIRTUAL TABLE embedding_fulltext USING fts5(id, string_value);


dir: metadb
filename: 00002-embedding-metadata.sqlite.sql
hash: 12a570f7121b3a8ce750a2a7c36da20f
sql:
-- SQLite does not support adding check with alter table, as a result, adding a check
-- involve creating a new table and copying the data over. It is over kill with adding
-- a boolean type column. The application write to the table needs to ensure the data
-- integrity.
ALTER TABLE embedding_metadata ADD COLUMN bool_value INTEGER


dir: metadb
filename: 00003-full-text-tokenize.sqlite.sql
hash: f97ad6334aeaa8f419f01110b648b97a
sql:
CREATE VIRTUAL TABLE embedding_fulltext_search USING fts5(string_value, tokenize='trigram');
INSERT INTO embedding_fulltext_search (rowid, string_value) SELECT rowid, string_value FROM embedding_metadata;
DROP TABLE embedding_fulltext;


dir: metadb
filename: 00004-metadata-indices.sqlite.sql
hash: fb36603a45ee2cd0254cef3ef86585e8
sql:
CREATE INDEX IF NOT EXISTS embedding_metadata_int_value ON embedding_metadata (key, int_value) WHERE int_value IS NOT NULL;
CREATE INDEX IF NOT EXISTS embedding_metadata_float_value ON embedding_metadata (key, float_value) WHERE float_value IS NOT NULL;
CREATE INDEX IF NOT EXISTS embedding_metadata_string_value ON embedding_metadata (key, string_value) WHERE string_value IS NOT NULL;


dir: metadb
filename: 00005-max-seq-id-int.sqlite.sql
hash: 0e9de46758761b373ce682925edcc326
sql:
ALTER TABLE max_seq_id ADD COLUMN int_seq_id INTEGER;

-- Convert 8 byte wide big-endian integer as blob to native 64 bit integer.
-- Adapted from https://stackoverflow.com/a/70296198.
UPDATE max_seq_id SET int_seq_id = (
  SELECT (
       (instr('123456789ABCDEF', substr(hex(seq_id), -1 , 1)) <<  0)
     | (instr('123456789ABCDEF', substr(hex(seq_id), -2 , 1)) <<  4)
     | (instr('123456789ABCDEF', substr(hex(seq_id), -3 , 1)) <<  8)
     | (instr('123456789ABCDEF', substr(hex(seq_id), -4 , 1)) << 12)
     | (instr('123456789ABCDEF', substr(hex(seq_id), -5 , 1)) << 16)
     | (instr('123456789ABCDEF', substr(hex(seq_id), -6 , 1)) << 20)
     | (instr('123456789ABCDEF', substr(hex(seq_id), -7 , 1)) << 24)
     | (instr('123456789ABCDEF', substr(hex(seq_id), -8 , 1)) << 28)
     | (instr('123456789ABCDEF', substr(hex(seq_id), -9 , 1)) << 32)
     | (instr('123456789ABCDEF', substr(hex(seq_id), -10, 1)) << 36)
     | (instr('123456789ABCDEF', substr(hex(seq_id), -11, 1)) << 40)
     | (instr('123456789ABCDEF', substr(hex(seq_id), -12, 1)) << 44)
     | (instr('123456789ABCDEF', substr(hex(seq_id), -13, 1)) << 48)
     | (instr('123456789ABCDEF', substr(hex(seq_id), -14, 1)) << 52)
     | (instr('123456789ABCDEF', substr(hex(seq_id), -15, 1)) << 56)
     | (instr('123456789ABCDEF', substr(hex(seq_id), -16, 1)) << 60)
    )
);

ALTER TABLE max_seq_id DROP COLUMN seq_id;
ALTER TABLE max_seq_id RENAME COLUMN int_seq_id TO seq_id;


dir: metadb
filename: 00006-metadata-array-support.sqlite.sql
hash: e026f01ea92c1baa1493f4ad5ca7cfe7
sql:
-- Separate table for exploded array metadata values.
-- Each array element gets its own row, enabling efficient $contains queries.
-- The existing embedding_metadata table (with its PRIMARY KEY (id, key))
-- remains untouched and continues to store scalar metadata values.

CREATE TABLE IF NOT EXISTS embedding_metadata_array (
    id INTEGER NOT NULL REFERENCES embeddings(id),
    key TEXT NOT NULL,
    string_value TEXT,
    int_value INTEGER,
    float_value REAL,
    bool_value INTEGER
);

CREATE INDEX IF NOT EXISTS embedding_metadata_array_id_key
    ON embedding_metadata_array (id, key);
CREATE INDEX IF NOT EXISTS embedding_metadata_array_key_string
    ON embedding_metadata_array (key, string_value) WHERE string_value IS NOT NULL;
CREATE INDEX IF NOT EXISTS embedding_metadata_array_key_int
    ON embedding_metadata_array (key, int_value) WHERE int_value IS NOT NULL;
CREATE INDEX IF NOT EXISTS embedding_metadata_array_key_float
    ON embedding_metadata_array (key, float_value) WHERE float_value IS NOT NULL;


dir: embeddings_queue
filename: 00001-embeddings.sqlite.sql
hash: d3755dfd232be8e8301f4d7fcfb3a486
sql:
CREATE TABLE embeddings_queue (
    seq_id INTEGER PRIMARY KEY,
    created_at TIMESTAMP NOT NULL DEFAULT CURRENT_TIMESTAMP,
    operation INTEGER NOT NULL,
    topic TEXT NOT NULL,
    id TEXT NOT NULL,
    vector BLOB,
    encoding TEXT,
    metadata TEXT
);


# collections

id: fb85bf48-961e-4953-9ab3-7fbd469f8358
name: my_docs
database_id: 00000000-0000-0000-0000-000000000000
config_json_str: {}
schema_str:
{"defaults":{"string":{"fts_index":{"enabled":false,"config":{}},"string_inverted_index":{"enabled":true,"config":{}}},"float_list":{"vector_index":{"enabled":false,"config":{"space":"l2","hnsw":{"ef_construction":100,"max_neighbors":16,"ef_search":100,"num_threads":4,"batch_size":100,"sync_threshold":1000,"resize_factor":1.2}}}},"sparse_vector":{"sparse_vector_index":{"enabled":false,"config":{"embedding_function":{"type":"unknown"},"bm25":false}}},"int":{"int_inverted_index":{"enabled":true,"config":{}}},"float":{"float_inverted_index":{"enabled":true,"config":{}}},"bool":{"bool_inverted_index":{"enabled":true,"config":{}}}},"keys":{"moddate":{"string":{"fts_index":{"enabled":false,"config":{}},"string_inverted_index":{"enabled":true,"config":{}}}},"#document":{"string":{"fts_index":{"enabled":true,"config":{}},"string_inverted_index":{"enabled":false,"config":{}}}},"producer":{"string":{"fts_index":{"enabled":false,"config":{}},"string_inverted_index":{"enabled":true,"config":{}}}},"trapped":{"string":{"fts_index":{"enabled":false,"config":{}},"string_inverted_index":{"enabled":true,"config":{}}}},"creator":{"string":{"fts_index":{"enabled":false,"config":{}},"string_inverted_index":{"enabled":true,"config":{}}}},"page":{"int":{"int_inverted_index":{"enabled":true,"config":{}}}},"#embedding":{"float_list":{"vector_index":{"enabled":true,"config":{"space":"l2","source_key":"#document","hnsw":{"ef_construction":100,"max_neighbors":16,"ef_search":100,"num_threads":4,"batch_size":100,"sync_threshold":1000,"resize_factor":1.2}}}}},"source":{"string":{"fts_index":{"enabled":false,"config":{}},"string_inverted_index":{"enabled":true,"config":{}}}},"total_pages":{"int":{"int_inverted_index":{"enabled":true,"config":{}}}},"creationdate":{"string":{"fts_index":{"enabled":false,"config":{}},"string_inverted_index":{"enabled":true,"config":{}}}},"page_label":{"string":{"fts_index":{"enabled":false,"config":{}},"string_inverted_index":{"enabled":true,"config":{}}}}}}


id: d67716a2-d18d-440e-8134-5f8eeb23f334
name: research
database_id: 00000000-0000-0000-0000-000000000000
config_json_str: {}
schema_str:
{"defaults":{"string":{"fts_index":{"enabled":false,"config":{}},"string_inverted_index":{"enabled":true,"config":{}}},"float_list":{"vector_index":{"enabled":false,"config":{"space":"l2","hnsw":{"ef_construction":100,"max_neighbors":16,"ef_search":100,"num_threads":4,"batch_size":100,"sync_threshold":1000,"resize_factor":1.2}}}},"sparse_vector":{"sparse_vector_index":{"enabled":false,"config":{"embedding_function":{"type":"unknown"},"bm25":false}}},"int":{"int_inverted_index":{"enabled":true,"config":{}}},"float":{"float_inverted_index":{"enabled":true,"config":{}}},"bool":{"bool_inverted_index":{"enabled":true,"config":{}}}},"keys":{"source":{"string":{"fts_index":{"enabled":false,"config":{}},"string_inverted_index":{"enabled":true,"config":{}}}},"page":{"int":{"int_inverted_index":{"enabled":true,"config":{}}}},"producer":{"string":{"fts_index":{"enabled":false,"config":{}},"string_inverted_index":{"enabled":true,"config":{}}}},"#embedding":{"float_list":{"vector_index":{"enabled":true,"config":{"space":"l2","source_key":"#document","hnsw":{"ef_construction":100,"max_neighbors":16,"ef_search":100,"num_threads":4,"batch_size":100,"sync_threshold":1000,"resize_factor":1.2}}}}},"creator":{"string":{"fts_index":{"enabled":false,"config":{}},"string_inverted_index":{"enabled":true,"config":{}}}},"author":{"string":{"fts_index":{"enabled":false,"config":{}},"string_inverted_index":{"enabled":true,"config":{}}}},"subject":{"string":{"fts_index":{"enabled":false,"config":{}},"string_inverted_index":{"enabled":true,"config":{}}}},"trapped":{"string":{"fts_index":{"enabled":false,"config":{}},"string_inverted_index":{"enabled":true,"config":{}}}},"keywords":{"string":{"fts_index":{"enabled":false,"config":{}},"string_inverted_index":{"enabled":true,"config":{}}}},"ptex.fullbanner":{"string":{"fts_index":{"enabled":false,"config":{}},"string_inverted_index":{"enabled":true,"config":{}}}},"title":{"string":{"fts_index":{"enabled":false,"config":{}},"string_inverted_index":{"enabled":true,"config":{}}}},"total_pages":{"int":{"int_inverted_index":{"enabled":true,"config":{}}}},"#document":{"string":{"fts_index":{"enabled":true,"config":{}},"string_inverted_index":{"enabled":false,"config":{}}}},"creationdate":{"string":{"fts_index":{"enabled":false,"config":{}},"string_inverted_index":{"enabled":true,"config":{}}}},"page_label":{"string":{"fts_index":{"enabled":false,"config":{}},"string_inverted_index":{"enabled":true,"config":{}}}},"moddate":{"string":{"fts_index":{"enabled":false,"config":{}},"string_inverted_index":{"enabled":true,"config":{}}}}}}


id: 5bde7647-1d90-469b-8f52-eedee5e23d35
name: gpt_research_watson3
database_id: 00000000-0000-0000-0000-000000000000
config_json_str: {}
schema_str:
{"defaults":{"string":{"fts_index":{"enabled":false,"config":{}},"string_inverted_index":{"enabled":true,"config":{}}},"float_list":{"vector_index":{"enabled":false,"config":{"space":"l2","hnsw":{"ef_construction":100,"max_neighbors":16,"ef_search":100,"num_threads":4,"batch_size":100,"sync_threshold":1000,"resize_factor":1.2}}}},"sparse_vector":{"sparse_vector_index":{"enabled":false,"config":{"embedding_function":{"type":"unknown"},"bm25":false}}},"int":{"int_inverted_index":{"enabled":true,"config":{}}},"float":{"float_inverted_index":{"enabled":true,"config":{}}},"bool":{"bool_inverted_index":{"enabled":true,"config":{}}}},"keys":{"page_label":{"string":{"fts_index":{"enabled":false,"config":{}},"string_inverted_index":{"enabled":true,"config":{}}}},"moddate":{"string":{"fts_index":{"enabled":false,"config":{}},"string_inverted_index":{"enabled":true,"config":{}}}},"#embedding":{"float_list":{"vector_index":{"enabled":true,"config":{"space":"l2","source_key":"#document","hnsw":{"ef_construction":100,"max_neighbors":16,"ef_search":100,"num_threads":4,"batch_size":100,"sync_threshold":1000,"resize_factor":1.2}}}}},"total_pages":{"int":{"int_inverted_index":{"enabled":true,"config":{}}}},"source":{"string":{"fts_index":{"enabled":false,"config":{}},"string_inverted_index":{"enabled":true,"config":{}}}},"producer":{"string":{"fts_index":{"enabled":false,"config":{}},"string_inverted_index":{"enabled":true,"config":{}}}},"subject":{"string":{"fts_index":{"enabled":false,"config":{}},"string_inverted_index":{"enabled":true,"config":{}}}},"creationdate":{"string":{"fts_index":{"enabled":false,"config":{}},"string_inverted_index":{"enabled":true,"config":{}}}},"trapped":{"string":{"fts_index":{"enabled":false,"config":{}},"string_inverted_index":{"enabled":true,"config":{}}}},"author":{"string":{"fts_index":{"enabled":false,"config":{}},"string_inverted_index":{"enabled":true,"config":{}}}},"keywords":{"string":{"fts_index":{"enabled":false,"config":{}},"string_inverted_index":{"enabled":true,"config":{}}}},"title":{"string":{"fts_index":{"enabled":false,"config":{}},"string_inverted_index":{"enabled":true,"config":{}}}},"ptex.fullbanner":{"string":{"fts_index":{"enabled":false,"config":{}},"string_inverted_index":{"enabled":true,"config":{}}}},"creator":{"string":{"fts_index":{"enabled":false,"config":{}},"string_inverted_index":{"enabled":true,"config":{}}}},"page":{"int":{"int_inverted_index":{"enabled":true,"config":{}}}},"#document":{"string":{"fts_index":{"enabled":true,"config":{}},"string_inverted_index":{"enabled":false,"config":{}}}}}}


id: 1862453a-1fb5-40bd-8b25-6a17f78a85ad
name: gpt_research_watson4
database_id: 00000000-0000-0000-0000-000000000000
config_json_str: {}
schema_str:
{"defaults":{"string":{"fts_index":{"enabled":false,"config":{}},"string_inverted_index":{"enabled":true,"config":{}}},"float_list":{"vector_index":{"enabled":false,"config":{"space":"l2","hnsw":{"ef_construction":100,"max_neighbors":16,"ef_search":100,"num_threads":4,"batch_size":100,"sync_threshold":1000,"resize_factor":1.2}}}},"sparse_vector":{"sparse_vector_index":{"enabled":false,"config":{"embedding_function":{"type":"unknown"},"bm25":false}}},"int":{"int_inverted_index":{"enabled":true,"config":{}}},"float":{"float_inverted_index":{"enabled":true,"config":{}}},"bool":{"bool_inverted_index":{"enabled":true,"config":{}}}},"keys":{"source":{"string":{"fts_index":{"enabled":false,"config":{}},"string_inverted_index":{"enabled":true,"config":{}}}},"#embedding":{"float_list":{"vector_index":{"enabled":true,"config":{"space":"l2","source_key":"#document","hnsw":{"ef_construction":100,"max_neighbors":16,"ef_search":100,"num_threads":4,"batch_size":100,"sync_threshold":1000,"resize_factor":1.2}}}}},"page_label":{"string":{"fts_index":{"enabled":false,"config":{}},"string_inverted_index":{"enabled":true,"config":{}}}},"total_pages":{"int":{"int_inverted_index":{"enabled":true,"config":{}}}},"author":{"string":{"fts_index":{"enabled":false,"config":{}},"string_inverted_index":{"enabled":true,"config":{}}}},"creator":{"string":{"fts_index":{"enabled":false,"config":{}},"string_inverted_index":{"enabled":true,"config":{}}}},"subject":{"string":{"fts_index":{"enabled":false,"config":{}},"string_inverted_index":{"enabled":true,"config":{}}}},"#document":{"string":{"fts_index":{"enabled":true,"config":{}},"string_inverted_index":{"enabled":false,"config":{}}}},"title":{"string":{"fts_index":{"enabled":false,"config":{}},"string_inverted_index":{"enabled":true,"config":{}}}},"creationdate":{"string":{"fts_index":{"enabled":false,"config":{}},"string_inverted_index":{"enabled":true,"config":{}}}},"trapped":{"string":{"fts_index":{"enabled":false,"config":{}},"string_inverted_index":{"enabled":true,"config":{}}}},"keywords":{"string":{"fts_index":{"enabled":false,"config":{}},"string_inverted_index":{"enabled":true,"config":{}}}},"page":{"int":{"int_inverted_index":{"enabled":true,"config":{}}}},"producer":{"string":{"fts_index":{"enabled":false,"config":{}},"string_inverted_index":{"enabled":true,"config":{}}}},"moddate":{"string":{"fts_index":{"enabled":false,"config":{}},"string_inverted_index":{"enabled":true,"config":{}}}},"ptex.fullbanner":{"string":{"fts_index":{"enabled":false,"config":{}},"string_inverted_index":{"enabled":true,"config":{}}}}}}


# embedding_metadata

key: chroma:document
string_value:
학생생활안내 학부과정 
Department of Design, College of Fine Arts
Seoul National University
서울대학교 미술대학 
디자인전공
2024


key: chroma:document
string_value:
목 차
디자인전공 소개
소속 및 명칭
세부전공 소개
1-1
1-2
1-3
1. 전공소개 및 학위명칭
산업디자인전공
시각디자인전공
2-1
2-2
2. 교수진 소개
3. 학사일반
등록,휴학,복학,퇴학,제적
장학,근로장학생
교환학생,방문학생
수강신청
공지확인
3-1
3-2
3-3
3-4
3-5
교과과정
졸업기준
세부전공의 선택
졸업전시 및 졸업
전과
부전공,복수전공,연합전공,교직
전공과목의 중복인정
4-1
4-2
4-3
4-4
4-5
4-6
4-7
4. 학교생활안내
디자인연구동(49동)
전시장
전산실
사진스튜디오
모형제작실
과방(학생 실기실)
5-1
5-2
5-3
5-4
5-5
5-6
5. 시설 및 장비


key: chroma:document
string_value:
4 5
디자인전공소개
디자인학부는 1998년 공예과와 산업디자인과를 통합하여 디자인학부로 개편하면서, 
‘21세기의 창의적 디자인 인재양성’과 ‘신 디자인가치 창조의 세계적 요람’을 교육의 
목표로 정립하였다. 또한 이를 달성하기 위한 실천전략으로 문화중심 교육과 다학제적 
교육, 개념창조 중심의 교육, 세계와의 협동 교육의 방향을 수립하였다. 
또한 대학원은 1996년 개교 50주년을 맞아 서울대학교가 수립한 장기발전구상에 
따라, 21세기 지식기반사회를 이끌어갈 ‘고급 디자인지도자의 양성’과 ‘세계적 수준의 
디자인연구 대학원’을 교육목표로 수립하고 이를 실천하기 위하여 21세기 문화연구와 
디자인산업을 선도하는 교육, 세계 디자인에 기여하는 실천 전략들을 수립하여 
진행하고 있다.
1. 전공소개 및 학위명칭
1-1
서울대학교 미술대학 디자인과 디자인전공
미술학사(디자인)
Bachelor of Fine Arts in Design
전공분리 전
전공분리 후


key: chroma:document
string_value:
Bachelor of Fine Arts in Design
전공분리 전
전공분리 후
서울대학교 미술대학 디자인과 
서울대학교 미술대학 디자인과 산업디자인전공
서울대학교 미술대학 디자인과 시각디자인전공
소속 및 명칭
1-2
◦ 명칭
◦ 학위의 
   명칭
◦ 소속
인간의 생활환경은 많은 구성원들로 이루어져 있으며 구성원들마다 서로간의 영향을 
주고받으며 한 사회를 만들어 나간다. 또한 인간, 도구, 공간, 문화 등은 서로가 조화를 
이루면서 한 사회를 만들어간다. 산업디자인 전공은 우리를 둘러싸고 한 사회와 
문화를 만드는 여러 도구, 수단, 공간과 현상을 대상으로 보다 삶의 가치를 높이며, 그 
필요성에 대하여 파악하고 이를 보다 창조적이며 미학적으로 형상화하는 창조행위로 
이루어진다. 이에 본 전공은 구조적, 기능적, 형태적 특성을 결정하는 여러 과정을 
통하여 인간, 도구, 공간 간의 조화를 추구하며, 제품디자인분야, 공간디자인분야,


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통하여 인간, 도구, 공간 간의 조화를 추구하며, 제품디자인분야, 공간디자인분야, 
운송수단디자인분야 등에서 형상화, 시각화시키고 있다. 이에 디자이너로서 갖추어야 
하는 창의력과 미적 능력을 함양할 수 있는 과정을 제공하여 다양한 분야에서 각자의 
가능성을 발휘할 수 있도록 한다.
시각디자인은 매스커뮤니케이션 시대의 다양한 정보와 인간의 의사와 사물의 이미지를 
보다 정확하고 합리적인 방법으로 전달하기 위한 창조행위이다.
그래픽디자인 분야는 정보를 시각화하여 전달하는 형식을 디자인하는 분야로서 
기본적으로 광고, 포스터, 타이포그래피, 일러스트레이션, 집단의 표시 및 행사의 
이미지 관리를 위한 여러 매체의 시각통일 전달기법 등을 주로 다루게 된다. 더 나아가 
미디어디자인분야는 영상편집디자인, 애니매이션, 인터랙션, UX 디자인, 서비스디자인 
등과 같은 21세기의 새로운 조형영역인 영상미디어분야와 관련된 교과목들을 제공하고 
있다.
◦ 산업디자인전공
◦ 시각디자인전공


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6 7
시각디자인 전공
2-1
담당분야 | 미디어디자인
세부전공 | 인터렉션디자인, UX디자인
이메일 | jheune@snu.ac.kr
URL | intermedia.or.kr
윤주현
담당분야 | 미디어디자인
세부전공 | 뉴미디어디자인
이메일 | suzung@snu.ac.kr
URL | www.suzung.com
김수정
담당분야 | 그래픽디자인
세부전공 | 그래픽, 타이포그래피
이메일 | ks1@snu.ac.kr
김경선
담당분야 | 그래픽디자인
세부전공 | 공공커뮤니케이션, 브랜딩
이메일 | leejangsub@snu.ac.kr
이장섭
담당분야 | 그래픽디자인
세부전공 | Computational Design
이메일 | chamamoto@snu.ac.kr
Chris Hamamoto
디자인전공 학과장
담당분야 | 공간디자인
세부전공 | 공간디자인
이메일 | sm.ahn@snu.ac.kr
안성모
산업디자인 전공
2-2
담당분야 | 제품디자인
세부전공 | User Interface


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산업디자인 전공
2-2
담당분야 | 제품디자인
세부전공 | User Interface
이메일 | parkym@snu.ac.kr
URL | cafe.naver.com/parkdesignlab
박영목
미술대학 학장
담당분야 | 제품디자인
세부전공 | UX디자인, HCI디자인
이메일 | jech@snu.ac.kr
URL | www.hcidlab.com
정의철
담당분야 | 리빙디자인
세부전공 | 가구, 생활제품디자인
이메일 | jang7047@snu.ac.kr
장성연
담당분야 | 디자인역사문화이론
세부전공 | 디자인 역사· 이론· 비평 
이메일 | getto@snu.ac.kr
URL | www.snu-dhc.com
김민수
2. 교수진 소개


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8 9
학생들은 소정의 서류를 제출하고 등록함으로써 학생의 신분을 취득하게 된다. 따라서 
제일 먼저 등록 절차를 밟는 것이 필요한데, 보통 1학기 등록은 2월말 경, 2학기 등록은 
8월말 경에 실시한다. 기한 내에 마이스누 > 등록,장학 > 등록에서 납부고지서를 
출력하여 납부하며, 1회 납부가 어려운 경우 분납을 통해서 4차례에 나누어 납부할 수 
있다.
2024년 현재 0학점 등록이 불가능하므로 1학점 이상 수강 신청 하여야 하며, 규정학기 
초과자 (9학기 이상 등록자)의 경우 별도의 기준을 통해 학점에 비례한 등록금을 
납부한다. 등록기간 중에 등록이나 휴학 허가를 받지 않으면 미등록 제적되며, 복적생, 
재입학생, 직전학기 휴학생은 등록 전에 복적, 재입학, 복학(귀)의 허가를 받은 후 
등록해야 한다.
◦ 등록
◦ 휴학의 제한 및 유의사항
학사과정의 휴학은 6학기를 초과할 수 없다. (질병휴학, 군휴학 불포함)


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학사과정의 휴학은 6학기를 초과할 수 없다. (질병휴학, 군휴학 불포함)
학기 중 휴학 한 학생(군입대 포함)의 성적은 그 교과목의 수강신청을 취소한 것으로 
처리한다. 그러므로 본인이 취소하지 않더라도 자동적으로 취소된다.
등록하기 전에 휴학원을 제출하여 허가를 받은 학생은 그 학기 납입금이 면제되고, 
등록 후 휴학 허가를 받은 학생은 복학할 때 등록금이 면제된다. 휴학기간은 재학연한에 
산입하지 아니한다.
등록, 휴학, 복학, 퇴학, 제적
3-1
학생이 부득이한 사유로 휴학하고자 할 때에는 마이스누 > 개인정보 > 휴복학신청 
메뉴에서 신청한다.  휴학기간은 기본적으로 1학기이며, 1학기 넘게 휴학을 원할 
경우에는 1학기 뒤에 다시 휴학 신청을 해야 한다.
• 일반휴학 : 등록금 납부기간 중에 등록금을 내지 않고 하는 휴학
• 학기 중 휴학 : 등록한 학생으로서 수업일수의 2/4 이전까지 휴학원을


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• 학기 중 휴학 : 등록한 학생으로서 수업일수의 2/4 이전까지 휴학원을  
                          제출하는 경우 (등록 후 수업 도중 휴학),  
                          이후에는 군휴학 또는 질병휴학만 가능 
• 종강 후 휴학 : 군복무. 군 대체복무 휴학에 한함
※ 산업기능요원 선발을 위해 휴학하는 경우 ① 지정 업체 확인서(병무청의  
   지정업체 선정 통보 공문 사본) ② 재직증명서 ③ 산업기능요원 편입예정확인서 
   (지정업체 발행)를 첨부하여 휴학을 신청. (산업기능요원 편입을 위해 휴학하는  
   학생은 제출서류를 첨부하여 일반휴학을 하고 편입 후 1주일 이내에  
   산업기능요원 복무기록표를 제출하여 군 휴학으로 전환하여야 함.)
복학시에는 마이스누 > 개인정보 > 휴복학신청 메뉴에서 복학을 신청한다.
◦ 휴학 절차
◦ 복학 절차
◦ 퇴학 및 제적


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◦ 휴학 절차
◦ 복학 절차
◦ 퇴학 및 제적
퇴학하고자 하는 학생은 보증인 연서로 그 사유를 기입하여 대학(원)장에게 제출하고 
대학(원)장의 허가를 얻어 퇴학할 수 있다. 퇴학 및 제적생은 등록 전에 복적, 재입학, 
복학(귀)의 허가를 받은 후 등록할 수 있다. 제적 기준은 다음과 같다.
1. 휴학기간이 초과된 경우에는 제적 된다. (병역복무을 위한 때에는 예외로 한다.)
2. 재학연한이 초과된 경우에는 제적 된다. (재입학 불가)
3. 등록기간 중 휴학 허가 없이 등록하지 않을 경우에는 제적된다.
4. 학사경고 4회 누적 시 학사위원회의 심의를 거쳐 제적된다.
   * 학사경고 : 평점평균 1.7 미달 / 3과목 이상 F / 6학점 이상 F
3. 학사일반


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10 11
• 장학금 신청 
장학금 신청기간에 서울대학교 포털(마이스누)를 통해 교내장학금을 신청한다.
(신청 완료한 학생만이 선정 대상).
※ 가계 곤란 장학금을 신청할 경우, 건강 보험 납부확인서 (부모 및 본인)
    부모 납부합계액이 10만원 이하일 경우에만 서류제출, 건강보험증 사본
• 수여기준 
전액면제와 등록금의 일정부분의 장학금이 있으며, 매 학기 배정 예산에 따라 수여하며 
수해 학생 수 및 금액에는 변동이 있다. 신청자에 한하여 심사를 하며 성적우수, 
가계곤란을 기준으로 심사한다. 금액의 배정은 세부전공별, 학년별로 배정한다.
• 장학금 수령
장학금은 등록금 고지서상에 표기, 장학금이 제외된 금액으로 고지서가 발급된다.
◦ 교내장학금
장학,근로장학생
3-2
• 근로장학생 신청절차
마이스누에서 신청 후 출력하여 학과사무실에 제출한다. 각 근무지별로 지원자 심사 후 
근로장학생을 선발한다. 
• 근로장학생의 업무


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근로장학생을 선발한다. 
• 근로장학생의 업무
1.  자신의 신청 부서에 따라서 다르지만 디자인전공 학과사무실에서는  
    보조업무 및 청소, 디자인, 정리 등을 담당한다.
2.  자신의 근무시간에 정해진 장소에서 근무해야하며 혹시 근무가 어려울 경우  
    미리 근무지 책임자에게 양해를 구하고 대신 근무를 할 인원을 책임지고  
    보충해야 한다. 
3.  근로장학생은 근무한 시간만큼 시간당 수당을 계산하여 매달 장학금을  
    지급받는다.
◦ 근로장학생


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12 13
국내 학점 교류 협정을 맺은 타 대학교에서 수학 후 학점을 인정받는 것이 가능하며, 
매 학기 마이스누 > 정보광장 > 게시판 > 학생공지사항에 공지된다. 공지사항 확인 후 
첨부파일에서 지원자 격 및 교류가능 대학을 확인한다.
• 수학기간 : 학사과정은 4개 학기, 석사과정과 박사과정은 2개 학기
• 계절학기 :  제한 없음
• 등록학기의 인정 : 수학기간 동안 본교에 소정의 등록금을 납입하여  
                                등록을 필하여야 함.
• 취득학점 : 학기당 취득학점(대부분 9학점)은 본교 취득학점을 포함  
                     17학점 이내 계절학기(6학점)는 9학점 이내로 함. 
※ 수학기간 동안 취득학점은 계절학기 취득학점을 포함하여 소속대학의  
   학위취득에 필요한 최저 소요학점의 2분의 1 이내로 함.
• 문의 : 대외협력실 02-880-7487 
◦ 국내 교환학생, 방문학생
교환학생, 방문학생
3-3


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◦ 국내 교환학생, 방문학생
교환학생, 방문학생
3-3
국내 학점 교류 협정을 맺은 타 대학교에서 수학 후 학점을 인정받는 것이 가능하며, 
매 학기 마이스누 > 정보광장 > 게시판 > 학생공지사항에 공지된다. 공지사항 확인 후 
첨부파일에서 지원자격 및 교류가능 대학을 확인한다.
• 지원자격 : 1개 학기 이상 등록한 학부생 
     기 이수한 학기의 성적 평점평균이 2.7(대학원 3.3 이상)이상.
     학칙상 징계 받은 사실이 없을 것.
     교환학생 파견 복귀 후 1개 학기 이상 본교 정규학기에 등록해야 함.
• 지원대학 : 본교와 학술교류협정을 체결한 외국대학(UCLA, Yale 등)
                    대외협력본부(OIA)를 통해 알 수 있음.
• 협정대학 : 알토 대학교(핀란드), 동경예술대학(일본), 브라이튼(영국),
                    네덜란드 헤이그 왕립예술대학(네덜란드)


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네덜란드 헤이그 왕립예술대학(네덜란드)
상기 대학들은 현재 미술대학, 미술대학 디자인학부와 협정을 맺은 대학들이며, 이와 관련한 
수학 신청 공지는 서울대학교 미술대학 홈페이지 게시판에 공지된다. 그 외 본부협정을 맺는 
대학으로의 국외 교환학생 신청은 대외협력본부 및 마이스누의 학생공지사항에 공지된다.
국외 교환학생의 경우, 귀국 후 과사무실에 성적표 및 수료확인서, 강의계획서, 수업시간표 
등을 제출하여 18학점 이내에서 학점 인정을 받을 수 있다. 이 경우 모두 일반선택(일선)교과로 
인정되며, 제출한 자료에 의하여 증명되지 않을 시에는 학점 인정이 불가하니, 되도록 타대 수학 
중 수업과 관련된 다양한 증명자료를 모아두는 것이 학점 인정에 많은 도움이 된다. 학점 인정은 
전적으로 서울대의 학점 기준으로 인정된다. 
이론 교과의 경우엔 1학점 당 15시간 이상 이수하여야 하며, 실기교과의 경우 1학점 당 30시간


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이상 이수하여야 한다. 최대 학점인정은 학기 당 18학점까지만 가능하며, 미술대학 국제교류(
외국대학 수학) 학생이 이수한 과목은 1과목당 최대 6학점까지 인정1)한다. 성적 또한 서울대에서 
사용하고 있는 (A,B,C,D,F)기준으로 인정되므로 학점 변환 테이블을 같이 제출하는 것이 좋다. 
학점 인정은 학사위원회를 거쳐야 하기에 6개월 이상 소요될 수도 있으므로, 미리 자료를 모아 
과사무실의 교무담당 조교에게 제출한다.
1) 2017년 11월 7일 개정
◦ 국외 교환/방문 학생


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출국 전 마이스누 학사정보 > 대외교류 > 국제교환학생 > 국제교환학생 프로그램 
신청에서 관련 서류를 스캔하여 업로드(PDF파일) 후 수학신청
• 신청시기 : 정규학기 수학은 수학 희망학기 3개월 전,  
                     계절학기 수학은 개강 2개월 전 신청
• 관련서류 : 지도교수 및 학과장 확인을 받은 수학신청원, 지도교수 추천서,  
                     교과구분확인서 (다전공관련 교과목)
• 서류제출 : 원본서류는 학과에 제출 및 스캔하여 포털에 직접 업로드
• 수강예정과목 미정의 경우 : 부득이하게 수강예정과목을 작성할 수 없을  
                                                경우에는 사유를 기재하여 확인 받은 수강신청원을  
                                                업로드(PDF파일)하고, 추후 수강과목이 정해지면


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포탈에서 국외수학변경신청을 해야함
• 다전공강의 수강 : 다전공과 관련된 교과목은 포탈 국외수학허가신청 화면에서  
                                교과구분확인신청서를 출력하여 다전공 학과사무실에서  
                                승인을 받아야함.
※ 국외수학허가와 관련된 공지는 미술대학홈페이지 게시판을 참고할 것. 
    관련 서류 및 자세한 신청 절차가 안내되어 있음. 
• 이수교과목은 전공관련 교과목을 원칙으로함
• 부득이하게 일반선택으로 이수할 경우 지도교수의 추천서 또는 특별한 사유를  
   기재한 학과장 확인서 제출 : 국외수학허가신청 또는 변경 신청시 제출
• 교양교과목 이수 불가
• 동일교과목 이수 불가
◦ 신청절차
◦ 교과목이수
• 변경절차 : 수강과목이 추후에 정해지거나 변경되는 경우에는 포탈에 관련서류를


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• 변경절차 : 수강과목이 추후에 정해지거나 변경되는 경우에는 포탈에 관련서류를  
                    스캔하여 업로드(PDF파일) 후 국외수학허가 변경 신청
• 관련서류 : 지도교수 및 학과장 확인을 받은 수학계획변경 내역서,  
                    교과구분확인서(다전공관련교과목)
• 수학기간 종료 후 즉시 학점인정 신청서, 교과목 강의시간 설명서를 작성하여  
   성적증명서 원본, 수업강의계획서 등 수업시간 및 수업주수를 확인할 수 있는  
   공식서류를 첨부하여 행정실에서 우선 검토 받은 후 지도교수와 학과장 확인 후 제출
• 학점계산 : 이론수업 15시간 1학점, 실기수업 30시간 1학점
• 이미 이수한 교과목과 동일한 교과목의 학점은 인정하지 아니함
✽서울대학교 학칙
제50조(수업일수) ①학사과정 및 대학원과정의 수업일수는 매학년 30주 이상으로 한다.


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제50조(수업일수) ①학사과정 및 대학원과정의 수업일수는 매학년 30주 이상으로 한다.
제74조(학점) ①교과목 이수의 단위는 학점으로 하고 1학기간 15시간 이상의 강의를 1
학점으로 하며, 실험·실습·실기 및 체육은 1학기간 30시간 이상의 수업을 1학점으로 한다.
◦ 변경신청절차
◦ 학점인정 신청절차


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수강신청을 하고 졸업기준에 맞는 학점을 취득하기 위해서는 우선 전공구분 및 
교과구분에 대한 이해를 명확히 하여야 한다. 교과구분은 수업의 성격을 의미하는 
것이며, 교양, 전선, 전필, 일선의 네 가지로 구분된다.
• 교양 : 교양 수업은 전공생뿐만 아니라 여타 다른 모든 학생들도 들을 수 있는  
             난이도의 수업으로 말 그대로 교양 함양을 위한 수업이다. 졸업하기  
             위해서는 대학 글쓰기, 대학영어 및 핵심 교양 포함 총 36학점 이상의  
             교양 수업을 들어야 한다. 교양 수업은 절대로 교과구분 수정이  
             불가하므로 수강 시 교양수업 여부를 확실하게 체크하여야 한다.  
             디자인전공에서 개설하는 교양수업으로는 ‘창조와 디자인’, ‘디자인과  
             생활’이 있으며, 이 수업들을 제외한 모든 디자인 전공의 수업은


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생활’이 있으며, 이 수업들을 제외한 모든 디자인 전공의 수업은 
             전공수업이다.
• 전선 : 디자인전공 개설 수업만을 전선수업으로 인정한다. 
• 전필 : 전선 수업들 중에서도 필히 들어야 할 교과목들은 전필로 지정되어 있다.  
             현재 디자인 학부 디자인전공의 전필교과목은  디자인기초1·2,  
             한국미술과 문화, 서양미술사, 디자인사, 미술대학 10개 공통 과목 중  
             4과목 선택 총 9개 교과목 21학점이다. 졸업하기 위해서는  
             전필교과목이수가 반드시 필요하다.
◦ 교과구분 및 전공구분
수강신청
3-4
디자인기초1 / 한국미술과 문화
미술대학 공통교과목 중 택 2 
전공필수 교과목
1학기
평면조형1 / 평면조형2 / 평면조형3 / 평면조형4  
입체조형1 / 입체조형2 
기초도예 / 기초금속공예 
기초산업디자인 / 기초시각디자인


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입체조형1 / 입체조형2 
기초도예 / 기초금속공예 
기초산업디자인 / 기초시각디자인 
디자인기초2 / 서양미술사 / 디자인사
미술대학 공통교과목 중 택 2 
2학기
평면조형1 / 평면조형2 / 평면조형3 / 평면조형4  
입체조형1 / 입체조형2 
기초도예 / 기초금속공예 
기초산업디자인 / 기초시각디자인 
졸업전시 수업 수강을 위한 세부전공별 전공필수이수교과목
공통
시각
산업
시각디자인 핵심이수교과목
산업디자인 핵심이수교과목
시각디자인 선택이수교과목 
산업디자인 선택이수교과목 
타이포그래피디자인1, UI디자인프로그래밍, 편집디자인, 정보인터랙션디자인
타이포그래피디자인2, VD컴퓨테이션, 아이덴티티디자인, 모션그래픽스
시각디자인기초, 애니메이션, 일러스트레이션, 영상디자인
디자인사진, 3D그래픽스, 서비스UX디자인
혼합매체드로잉, 사물인터랙션디자인, 바이오디자인
혼합재료모델링, 운송디자인, 공간디자인
디자인기초1, 디자인기초2, 디자인사


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혼합재료모델링, 운송디자인, 공간디자인
디자인기초1, 디자인기초2, 디자인사
디자인요소와원리, 공간디자인기초, 제품서비스디자인, 리빙디자인
제품디자인, 구조와재료디자인, 인간행태와디자인, 사회적이슈와디자인
1학기
2학기
1학기
2학기
1학기
2학기
1학기
2학기
* 2024학번부터는 아래의 세부전공별 요건을 이수한 학생만이 졸업전시 관련 수업을 수강할 수 있음.
* 졸업전시 이전에 학생이 해당하는 전공의 핵심이수교과목 중 최소 6과목을 반드시 포함하여,  
  핵심이수교과목+선택이수교과목 총 8과목 이상을 이수해야 함.
* 복수전공으로 진입한 학부생(자율전공학부생 포함)은 디자인기초1·2,디자인사 및 해당하는  
  전공의 핵심이수(최소 6과목)+선택이수 총 8과목 이상 이수하여야만 졸업전시 관련 수업을  
  수강할 수 있음.
* 2024년 이전 학번의 경우, 본인 학번에 해당하는 기존의 세부전공별 요건 또는 신설된


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세부전공별 요건에 포함되어 있는 개별 과목들을 자율적으로 선택하여 핵심이수(최소 6과목)+ 
  선택이수 총 8개 이상 과목을 맞추어 이수할 수 있음. (다음 페이지 참조)
* 2024년 이전 학번의 경우, 본인 학번에 해당하는 기존의 세부전공별 요건 또는 신설된  
  세부전공별 요건 중 한 곳이라도 핵심/선택이수교과목으로 분류되는 경우, 수강 학기와 상관  
  없이 핵심/선택이수교과목으로 분류 가능
* 최소 6과목 이상 이수
* 최소 6과목 이상 이수
* 핵심이수교과목+선택이수교과목 8과목 이상 이수
* 핵심이수교과목+선택이수교과목 8과목 이상 이수


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18 19
졸업전시 수업 수강을 위한 세부전공별 전공필수이수교과목
공통
시각
산업
시각디자인 핵심이수교과목
산업디자인 핵심이수교과목
시각디자인 선택이수교과목 
산업디자인 선택이수교과목 
시각디자인기초, 애니매이션, 편집디자인, 정보인터랙션디자인
타이포그래피디자인, 미디어디자인프로그래밍, 아이덴티티디자인, 모션그래픽스
일러스트레이션1, 영상디자인
일러스트레이션2, 공공커뮤니케이션디자인, 미디어인터랙션디자인
디자인과정과방법, 주거공간디자인, 사물인터랙션디자인
운송기기디자인
디자인기초1, 디자인기초2, 디자인사
디자인의요소와원리, 공간디자인컨셉스터디, 제품디자인, 사회적이슈와디자인
제품리버스디자인, 구조와재료디자인, 인간행태와디자인, 도시공간디자인
1학기
2학기
1학기
2학기
1학기
2학기
1학기
2학기
◦ 직전 내규의 세부전공별 이수교과목 (2020~2023학번)
* 2024년 이전 학번의 경우, 본인 학번에 해당하는 기존의 세부전공별 요건 또는 신설된


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세부전공별 요건에 포함되어 있는 개별 과목들을 자율적으로 선택하여 핵심이수(최소 6과목)+ 
  선택이수 총 8개 이상 과목을 맞추어 이수할 수 있음.  
* 2024년 이전 학번의 경우, 본인 학번에 해당하는 기존의 세부전공별 요건 또는 신설된  
  세부전공별 요건 중 한 곳이라도 핵심/선택이수교과목으로 분류되는 경우, 수강 학기와 상관  
  없이 핵심/선택이수교과목으로 분류 가능
• 일선 : 위의 교과구분에 해당하지 않는 수업, 즉 미대 외부 타과의 전공수업들은  
              모두 일반 선택에 해당한다. 상대적으로 미대 외부의 학생들에게는  
              미술대학의 전공수업이 일반 선택으로 적용된다. 성적표에 미술대학  
              수업인데 일선으로 표기되었을 경우에는 졸업하기 전에 전선 혹은 전필로  
              교과과정 정정을 신청한다.


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교과과정 정정을 신청한다.
• 전공구분 : 주전공, 복수전공, 연계전공 등 현재 본인이 택한 전공 트랙을  
                    의미한다. 기본적으로 디자인전공을 졸업하기 위해 듣는 수업은  
                    모두 주전공에 해당한다. 이 외 복수전공을 이수하면서 듣는 수업은  
                    전공구분을 복수전공으로 표기하여야 한다. 복수전공시 전공구분을  
                    명확히 표기하여야 복수전공 이수학점이 정상적으로 전산 처리된다.  
                    마이스누 > 누적성적 > 교과구분변경에서 전공구분을 신청한 후  
                    출력하여 학과사무실에 제출하면 된다. 
수강예시 1 (시각디자인전공) - 핵심이수교과목 8과목
 타이포그래피디자인1, UI디자인프로그래밍, 편집디자인, 정보인터랙션디자인,


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타이포그래피디자인1, UI디자인프로그래밍, 편집디자인, 정보인터랙션디자인,  
 타이포그래피디자인2, VD컴퓨테이션, 아이덴티티디자인, 모션그래픽스
수강예시 2 (시각디자인전공) - 핵심이수교과목 6과목, 선택이수교과목 2과목
 타이포그래피디자인1, UI디자인프로그래밍, 편집디자인, 정보인터랙션디자인, 아이덴티티디자인,  
 모션그래픽스, 일러스트레이션,  3D그래픽스
수강예시 3 (시각디자인전공, 2024학번 이전) - 핵심이수교과목 6과목, 선택이수교과목 2과목
 시각디자인기초(기존 핵심이수교과목), 타이포그래피디자인(기존 핵심이수교과목), 편집디자인,  
 정보인터랙션디자인, 아이덴티티디자인, 모션그래픽스, 일러스트레이션,  3D그래픽스
수강예시 4 (산업디자인전공) - 핵심이수교과목 8과목 
 디자인요소와원리, 공간디자인기초, 제품서비스디자인, 리빙디자인, 제품디자인, 구조와재료디자인,  
 인간행태와디자인, 사회적이슈와디자인 (핵심이수교과목 8과목)


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인간행태와디자인, 사회적이슈와디자인 (핵심이수교과목 8과목)
수강예시 5 (산업디자인전공) - 핵심이수교과목 6과목, 선택이수교과목 2과목 
 공간디자인기초, 제품서비스디자인, 리빙디자인, 제품디자인, 구조와재료디자인, 인간행태와디자인,  
 바이오디자인, 운송디자인
수강예시 5 (산업디자인전공, 2024학번 이전) - 핵심이수교과목 6과목, 선택이수교과목 2과목 
 제품리버스디자인(기존 핵심이수교과목), 공간디자인컨셉스터디(기존 핵심이수교과목), 리빙디자인,  
 제품디자인, 구조와재료디자인, 인간행태와디자인, 바이오디자인, 운송디자인


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20 21
학사일정표를 통해 수강신청 기간을 알 수 있으며 포털 마이스누 > 학사정보 > 
나의정보 > 종합정보 > 학사일정 페이지를 참고한다. 수강신청 기간에는 마이스누에서 
바로 수강신청 페이지로 이 동할 수 있다. 수강 신청은 총 18학점까지 가능하며, 
직전학기 2학기 평점 3.3 이상일 경우 21학 점까지 신청 가능하다. 수강신청 시 
교과구분, 전공구분을 명확히 하도록 한다. 교과구분은 전선, 전필, 교양을 말하는 
것이며, 전공구분은 주전공, 복수전공, 연계전공 등을 의미한다. 수강신청 시 전공구분, 
교과구분을 명확히 하지 않으면 졸업 시 남은 학점을 정확하게 계산하기 어렵다.
수강신청기간에 수강신청하지 못한 교과목은 개강 후 1주일 간의 변경기간 동안 
수강정원외신청이 가능하다. sugang.snu.ac.kr > 수강신청 > 정원 외 신청 > 정원 
외 신청서 작성을 통해 신청이 가능하며, 담당 강사의 승인을 통해 수강이 확정된다.  
* 정원 외 신청 가능한 경우


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* 정원 외 신청 가능한 경우
 - 수강반 제한 등으로 정원이 마감되지 않았더라도 수강신청 할 수 없는 경우
 - 정원이 마감되어 수강신청 할 수 없는 경우
 - 신청가능학점 : 수강신청가능학점 수강신청한 학점 + 6 학점
 - 수강확정가능학점 : 수강신청 가능 학점 이내
◦ 수강신청
◦ 수강정원외신청
◦ 수강신청취소
수강신청 취소는 수업주수 1/2선까지 가능하다. 정확한 날짜는 포털 마이스누 > 
학사정보 > 나의정보 > 종합정보 > 학사일정 페이지를 참고한다. 개강 후 일주일동안의 
변경기간에는 임의로 취소가 가능하며, 변경기간이 지난 후에는 수강신청사이트가 
아닌 마이스누에서 취소신청을 한 후, 취소원을 출력하여 담당교수님께 허락을 받고 
과사무실에 제출한다. 디자인전공 수업의 경우에는 조교와의 협의를 통해 사인 
과정을 생략하거나 담당 교수와의 통화를 통해서도 가능하지만, 디자인 전공이 아닌 
외부수업의 경우 반드시 담당 강사의 사인 혹은 통화내역을 증명해야 한다.


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외부수업의 경우 반드시 담당 강사의 사인 혹은 통화내역을 증명해야 한다.
※취소원 제출 후에는 반드시 마이스누에서 교과목 추가 삭제여부를 확인 한다.
개강 후 2주일이 지나면 대부분 수강신청이 확정되며, 담당 교수들은 확정 출석부를 
통해서 출석을 확인하게 된다. 하지만, 대부분 선생님들은 확정 출석부를 사용하지 
않고, 기존의 출석부를 통해서 출석을 확인하기 때문에 반드시 마이스누에서 본인의 
수강신청 여부를 확인하여야 한다. 수업주수 1/2선, 즉 수강신청 취소기간이 지난 
이후에는 어떠한 변경도 불가하므로 이후의 수강신청 관련 문제는 결국 학생의 책임이 
된다. 따라서 철저히 개강 후 한 달 안에는 본인의 수강신청 내역을 확인하도록 한다.
◦ 수강신청내역확인
◦ 계절학기
계절학기 수업은 학칙상의 정규학기와는 구분된다. 계절학기 수업에서 제공하는 
교과목을 이수하여 취득할 수 있는 학점은 여름/9, 겨울/6학점까지이며, 취득한 학점은


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교과목을 이수하여 취득할 수 있는 학점은 여름/9, 겨울/6학점까지이며, 취득한 학점은 
학칙에 규정된 학사과정 이수학점에 포함하되, 계절학기는 학칙상의 수업연한에는 
포함하지 아니한다.
• 수강신청기간 : 매년 5월 중, 11월중
• 성적평점평균 : 계절학기에 취득한 교과목의 성적 평점평균은 당해 학년도의  
                            정규학기 성적 평점평균에는 합산하지 아니하며, 졸업학점  
                            계산에는 포함한다.


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22 23
등록, 성적, 휴학 등 대부분의 학사행정이 마이스누(my.snu.ac.kr)에서 이루어진다. 
따라서 마이스누 ID 및 패스워드는 반드시 기억해야 하며, 마이스누를 자주 확인하여 
다양한 정보를 얻도록 하는 것이 좋다. 
▷ 학사행정 : 수강편람조회, 성적조회, 장학금신청, 등록 관련 조회, 휴복학 신청 등  
                      가장 빈번하게 사용되는 메뉴
▷ 웹메일 : 학교에서 유용한 공지가 SNU메일로 오니 확인할것.
▷ 정보광장 : 학생 공지사항 게시판에서는 장학, 교류 등 모든 학생관련 정보를  
                      조회할 수 있다. 
• 가입방법 : 학번이 부여된 후, 주민등록번호를 사용하여 my.snu.ac.kr에서 
                    신청 및 발급이 가능하다. 
• 기본정보 등록 : 학교에서 취해지는 연락은 마이스누를 기초로 이뤄지니 자신의


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• 기본정보 등록 : 학교에서 취해지는 연락은 마이스누를 기초로 이뤄지니 자신의  
                             연락처, 주소 등 기본 정보를 성실히 기입하고 변경하여 연락이  
                             원활히 이뤄지도록 한다. 주소나 연락처가 바뀌는 경우 반드시  
                             변경한다.
• 비밀번호 분실시 : 만일 온라인을 통해서 비밀번호 파악이 안 될 경우,  
                                중앙전산원(02-880-8282)에 직접 방문하여 본인의  
                                신분증을 제시하고 비밀번호를 재설정해야 한다.
◦ 마이스누
공지확인
3-5
디자인전공과 관련된 장학 등의 모든 공지사항은 미술대학 홈페이지의 
디자인전공 공지사항 게시판에 공지되므로 중요한 공지를 찾아서 보는 
것이 중요하다. 
art.snu.ac.kr


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것이 중요하다. 
art.snu.ac.kr
디자인전공과 관련된 공지사항 / 행사안내 / 공모전 / 구인 정보를 
제공한다. 학부 내에서 긴급하게 이루어지는 학사 및 취업 관련 공지는 
일반적으로 페이스북을 통해서 공지된다. 미술대학 홈페이지의 공지도 
페이스북을 통해 공지되니 자주 확인하여 중요한 정보를 놓치는 일이 
없도록 한다. 
www.facebook.com/designsnu
서울대학교 전체의 대표적인 학생 커뮤니티. 다른 과 학생들의 
생각이나 동아리활동, 월세방 정보 등 모든 과 학생들의 생각을 들여다 
볼 수 있는 곳이다. 또한 배달음식점 정보를 제공하고 있다. 
www.snulife.com
◦ 미술대학 홈페이지
     (미술대학 및 디자인전공 게시판)
◦ 디자인전공 페이스북(학부)
◦ 스누라이프


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교과과정
4-1 교과과정의 전체적인 구성을 참고하여 기본적으로 이수해야 할 가이드에 따라 선수과목을 이수한 후 다음 수업을 듣도록 한다. 
디자인전공 교과과정의 기본적인 틀은 다음과 같다. 
4. 학교생활안내


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◆ 디자인전공 소속학생
서울대학교의 학부 졸업기준 학점은 총 130학점이며, 
평균학점 2.00점 이상이어야 졸업 가능하다.
디자인전공 개설 전공수업을 57학점 이상 이수하여야 하며, 
전공필수교과목을 필수로 이수해야 한다. (미술대학 
공통교과목을 포함한 전공필수교과목은 57학점에 포함됨) 
디자인전공의 전공 학점을 69학점 이상 이수하여야 하며,  
전공필수교과목을 필수로 이수해야 한다. (미술대학 
공통교과목을 포함한 전공필수교과목은 69학점에 포함됨) 
교양 학점은 총 36학점 이상 이수하여야 하며 대학 글쓰기, 
대학영어를 포함한 학문의 기초 8학점 이상, 이외 학문의 기초  
4개 분야별 3학점 이상(12학점) 이수하여야 한다.
6학점 이상 이수하여야 하며, 어떤 강좌든 수강편람 우측에 'e' 
표시가 되어 있는 교과는 영어강의로 인정된다. 현재 디자인 
전공의 영어강의는 'UI디자인프로그래밍' 및 '디자인사고및연구' 
강좌가 영어진행 강의에 해당한다.


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강좌가 영어진행 강의에 해당한다.
졸업전시를 위해서는 각 세부전공이 정한 전공필수과목을 모두 
이수한 후, 매년 초 4학년 졸업전시 교과를 해당 교수에게 수강 
허락을 받고 수강하여야 한다.
이후 각 세부전공이 정한 절차에 따라 예비발표를 진행하고, 
졸업전시 직전 담당 교수님들이 참여하시는 졸업심사를 통과한 
자에 한하여 졸업전시가 가능하다.
졸업기준
4-2
다전공 선택할 경우
심화전공 경우
교양학점
영어진행강의
졸업전시
◆ 타전공 소속학생
디자인전공 복수전공자
디자인전공 부전공자
디자인전공 개설 전공 학점을 57학점 이상 이수하여야 
하며, 전공필수교과목을 필수로 이수해야한다. (미술대학 
공통교과목을 포함한 전공필수교과목은 57학점에 포함됨) 
* 미술대학 내 복수전공자는 필수과목을 9학점까지 중복 인정 
'디자인사' 교과목을 포함한 디자인전공 학점 21학점 이상 
이수해야 한다.
* 부전공은 1학년 전공선택수업 이수시 '기초산업디자인(입체조형(005))',


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기 결정된 세부전공은 원칙적으로 변경될 수 없으나 특별한 사유가 있을 경우 
세부전공분리 신청을 받는 기간에 세부전공 변경신청서 및 사유서(자유양식)을 제출한 
후 전공교수회의에서의 심사를 통과하여야 하며, 아래 경우에 한하여 변경할 수 있다.
1.
2.
3.
◦ 세부전공 변경
신청자격
신청시기
2개 학기를 이수한 학생을 대상으로 하며, 1학년 기초과정의 8개 
필수 교과목을 모두 이수한 자. 총 이수학점이 교양과목 포함 30
학점 이상인 자.
매년 초 정확한 시기는 디자인과 홈페이지를 통해 공지된다. 
일년에 한 번 있으므로 2학년 진입시 반드시 공지를 참고하여 
세부전공 분리를 신청하도록 한다.
디자인전공에 입학한 모든 학생은 1학년 기초과정을 이수한 후 다음의 선발 절차에 
의하여 2학년 진입 시 자신의 세부전공을 결정하여야 한다. 특정 세부전공을 
선택한 학생수가 각 세부전공에서 정한 수용능력을 초과하는 경우에는 디자인전공


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선택한 학생수가 각 세부전공에서 정한 수용능력을 초과하는 경우에는 디자인전공 
교수회의에서 정한 성적평점(교양과목 포함한 2개 학기 평점평균) 선발기준에 따른다. 
학생들은 소속 세부전공에서 요구하는 이수 지침을 충족시켜야 졸업할 수 있다.
세부전공의 선택
4-3
* 산업디자인전공 졸업전시교과목 수강 전 선수교과
◦ 세부전공 선수교과
세부전공을 선택한 이후에는 각 전공별 선수과목을 이수하여야 졸업전시 교과를 
이수하고 졸업할 수 있으며 각 세부전공별 선수과목은 다음과 같다. 졸업전시 교과목 
수강직전 과사무실에 성적증명서와 수학계획서를 받아 전공교수에게 자격을 허락 받은 
자 만이 졸업전시 수업을 수강할 수 있다. 
* 시각디자인전공 졸업전시교과목 수강 전 선수교과
디자인기초1
디자인기초2
디자인사
+ 졸업필수교과목 디자인사, 한국미술과문화, 서양미술사. 디자인기초1, 디자인기초 2, 미술대학공통교과목 10개 중 택 4


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+ 졸업필수교과목 디자인사, 한국미술과문화, 서양미술사. 디자인기초1, 디자인기초 2, 미술대학공통교과목 10개 중 택 4
* 졸업전시 이전에 학생이 해당하는 전공의 핵심이수교과목(6과목 포함)+선택이수교과목에서 
  총 8과목 이상을 이수해야 함.
* 졸업전시 이전에 학생이 해당하는 전공의 핵심이수교과목(6과목 포함)+선택이수교과목에서 
  총 8과목 이상을 이수해야 함.
디자인기초1
디자인기초2
디자인사
전공필수이수교과목
전공필수이수교과목
공통
공통
핵심이수교과목
핵심이수교과목
선택이수교과목
선택이수교과목
졸업전시교과목
졸업전시교과목
그래픽디자인프로젝트1·2
브랜드디자인프로젝트1·2
미디어디자인프로젝트1·2
UI,UX디자인프로젝트1·2
제품인터랙션디자인프로젝트1·2
모빌리티디자인프로젝트1·2
리빙디자인프로젝트1·2
공간디자인프로젝트1·2
타이포그래피디자인1
타이포그래피디자인2
UI디자인프로그래밍
VD컴퓨테이션
편집디자인
아이덴티티디자인
정보인터랙션디자인
모션그래픽스


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VD컴퓨테이션
편집디자인
아이덴티티디자인
정보인터랙션디자인
모션그래픽스
시각디자인기초
애니메이션
디자인사진
일러스트레이션
영상디자인
3D그래픽디자인
서비스UX디자인
디자인요소와원리
공간디자인기초
제품디자인
구조와재료디자인
제품서비스디자인
리빙디자인
인간행태와디자인
사회적이슈와디자인
혼합매체드로잉
혼합재료모델링
사물인터랙션디자인
바이오디자인
운송디자인
공간디자인
▶
▶
세부전공변경 직전 2개 학기의 전공실기 교과목 평점이 3.0 이상이어야 한다.
학생들은 반드시 세부전공변경 신청서(미술대학 디자인전공 게시판 참고) 및
변경사유서(자유양식)를 학부사무실에 제출한다.
변경 신청자는 디자인전공 교수회의에서 협의하여 최종 결정한다.


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30 31
◦ 졸업전시
◦ 졸업의 절차
졸업전은 매년 12월에 있으며, 4학년은 졸업전시 준비위원회를 자체적으로 조직하여 
전시 6개월 전에 과사무실에 알린다. 전시는 일반적으로 49동 내에서 진행된다. 
졸업전시에 참여하기 위해서는 세부전공 별로 졸업전시교과목을 2개 이상 이수하여  
2개 이상의 작품을 졸업심사에 출품하고 심사를 통과하여야 한다.
해당 학기에 졸업을 희망하는 학생들은 개강 이전 졸업사정기간에 
학과사무실을 방문하여 졸업사정신청원 및 누적성적증명서와 마지막 
학기 수강신청계획원을 제출하고 담당조교에게 필히 졸업 사정을 
받아야 한다. 
교과구분이 정확히 기재되어있는 것은 졸업의 전산처리에 
중요한 부분이다. 교과구분이 정확하지 않을 경우, 마이스누에서 
교과구분정정신청을 한 다음 신청서를 출력하여 학과사무실에 제출.
※교과구분 정정이 제대로 되지 않은 경우, 온라인상 졸업불가로 
판정되며 졸업이 불가할 수 있다.


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판정되며 졸업이 불가할 수 있다.
졸업신청기간에 마이스누에서 졸업을 신청한 다음 졸업신청서를 
과사무실에 제출한다. 
시각디자인전공 졸업전시 교과목
(4과목 중 2과목 선택)
그래픽디자인프로젝트1·2
브랜드디자인프로젝트1·2
미디어디자인프로젝트1·2
UI,UX디자인프로젝트1·2
산업디자인전공 졸업전시 교과목
(4과목 중 2과목 선택)
제품인터랙션디자인프로젝트1·2
모빌리티디자인프로젝트1·2
리빙디자인프로젝트1·2
공간디자인프로젝트1·2
졸업사정
(~개강첫주)
교과구분정정
졸업신청
졸업전시 및 졸업
4-4
전과란, 본교 내에서 학생의 학과(부) 소속을 변경하여 다른 학과(부)에 소속함을 
말한다. 전과(부)는 매 학년도 말에 한하여 4회 이상 등록하고 제2학년 수료학점 (66
학점) 이상, 제3학년 수료 학점 (98학점)에 미달되는 자로 한다. 전 · 출입 인원은 학과(
부)별 입학 정원의 100분의 20 이내로 한다. 사범계 이외의 학과(부)는 사범계 이외의


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각 학과(부)간 (다만 의학계, 치의학계, 약학계, 수의학계, 간호학계로의 전과(부)는 
제외) 전과가 가능하다.
전과(부)를 하고자 하는 자는 소정의 원서와 소속 대학장의 승인서, 
성적증명서를 첨부하여 지원대학장에게 제출한다.
전과(부)를 하고자 하는 자는 소속학과(부)장의 승인서와 
성적증명서를 첨부하여 소정의 원서를 소속대학장에게 제출한다.
전과(부)한 자의 재학연한은 종전의 재학연한을 통산한다.
전과(부)후의 교과목 이수는 전입학과(부)의 교과목 이수 원칙을 
따른다. 전과(부)는 1개 학과(부)에 한하여 지원 가능함 
(이중지원 불가)
소속대학 외
소속대학 내
유의사항
전과
4-5


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32 33
2007년 이후에 입학한 학생들은 제2전공 및 심화전공이 필수사항이므로, 위의 
학점배분표에 기준하여 제2전공 혹은 심화전공을 선택하도록 하며, 별도로 제2
전공을 신청하지 않았을 경우 자동으로 심화전공으로 결정된다. 복수, 연합, 부전공의 
이수기준은 전적으로 해당학과의 기준을 따른다. 따라서 제2전공 선택시 해당학과에 
문의하여 이수기준을 반드시 확인하여야 한다.
복수전공이란 학생들의 교과목 선택의 폭을 넓히고 지식의 편협함을 지양하여 넓은 
학문적 시야를 갖출 수 있도록 하는 것으로써, 소속 전공과정을 포함하여 2개 이상의 
전공과정을 이수하는 것을 말한다.
복수전공이수자는 해당 학과(부)에서 정한 전공과목 학점을 이수하여야 하고, 성적 
평점평균이 2.7 이상이어야 한다. 복수전공시 복수전공 학과(부)의 교과과정에서 
전공교과목으로 인정하는 경우(단과대학 내의 공통교과목 포함)에는 9학점까지, 소속


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전공교과목으로 인정하는 경우(단과대학 내의 공통교과목 포함)에는 9학점까지, 소속 
전공학과(부)와 복수전공 학과(부)의 교과과정 에서 공통으로 인정하는 타 학과(부) 
교과목(단과대학 내의 공통교과목 포함)은 3학점까지 중복 인정할 수 있다.
학부.학과(학과(부)내 전공과정 포함)를 원칙으로 함.
학과(부)의 졸업에 필요한 최저학점의 2분의 1 이상 취득한 이후
개학 한 달전 공지 참고하여 복수전공 신청서와 구비서류를 소속 학과
(부)장의 승인을 받아 단과대학 내 복수전공인 경우에는 학장에게, 
단과대학간 복수전공인 경우에는 학장을 경유하여 복수전공을 
이수코자 하는 대학에 제출한다.
◦ 복수전공
적용범위
지원자격
신청절차
부전공, 복수전공, 연합전공, 교직
4-6
교양 (36) 복수전공
(전공별내규) 자유
전공 (57)
전필 :21학점포함
전선 :디자인개설과목


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(전공별내규) 자유
전공 (57)
전필 :21학점포함
전선 :디자인개설과목
0                    36                                         93                    130
부전공이란 오늘날 학문이 점차 상호 교류를 지향하고 있는 추세에 비추어서 자기 전공 
이외 분야에 대한 학생들의 학구 의욕을 충족시키기 위하여 타 학과 전공과목을 일정 
수 이상 체계적으로 이수하는 과정을 말한다. 따라서 현재 전공하는 분야에 만족하지 
못하거나 다양성 있게 학문을 하고자 하는 경우 부전공을 이수하는 것이 좋을 것이다.
사범대학 각 학과, 농업생명과학대학 농산업교육과 및 교직과정이 설치된 학과 
상호간에 가능하며, 이외의 학과(부) 학생들은 위 학과의 부전공을 이수할 수는 있으나 
교원자격증은 취득할 수 없다. 단, 교원자격증의 부전공 표기를 원하는 경우는 부전공 
학과의 교과교육론 및 교과교재 연구 및 지도법을 포함한다.


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학과의 교과교육론 및 교과교재 연구 및 지도법을 포함한다.
인문·사회계 각 학과(부)와 자연과학대학의 수리과학부, 통계학과 및 
사범대학 수학교육과
인문·사회과학·자연과학계 대학 전 학과(부) 
소속대학의 각 학과(부)
의예·수의예·의학·치의학·수의학·간호학과는 부전공을 이수할 수 
없으며, 타 학과(부) 학생도 위 학과를 부전공 할 수 없음.
33학점 이상 취득자
개학 한 달전 공지 참고하여 부전공 지원서와 성적증명서를 소속 학과
(부)장의 승인을 받아 학장을 경유, 부전공을 이수하고자 하는 대학에 
제출한다.
◦ 부전공
※ 교원자격증 취득과 관련된 부전공 이수
인문사회계
자연계
예능계
불가학과
지원자격
신청절차
교양 (36)
부전공
연계전공
(전공별내규)
자유
전공 (57)
전필 :21학점포함
전선 :디자인개설과목
0                    36                                         93                    130


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34 35
2개 이상의 학과(부) 전공이 연합하여 교과과정을 편성, 제공하거나 학생이 교과과정을 
구성하여 대학의 인정을 받은 것을 말한다.
4개 학기 이상 6개 학기 이내 이수한 경우인 자. 직전학기 성적 
평점평균으로 2.7 이상.
연합전공 참여학과 재학생.
부전공 및 복수전공 이수자는 지원할 수 없음.
연합전공 해당 학부(과)에서 정한 전공학점을 최소 39학점 이상 
이수(단, 각 전공에 중복되는 교과목은 인정하지 않음)하고, 성적 
평점평균이 2.0 이상이어야 한다. 
개학 한달 전 공지를 참고하여 지도교수, 학과장, 해당 대학을 경유하여 
연합전공 주임에게 신청서 제출.
취소원은 매학기 개시 2주 전 연합전공 주임에게 제출.
◦ 연합전공
지원자격
지원자격
신청절차
교양 (36) 연합전공
(전공별내규) 자유
전공 (57)
전필 :21학점포함
전선 :디자인개설과목


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(전공별내규) 자유
전공 (57)
전필 :21학점포함
전선 :디자인개설과목
0                    36                                         93                    130
복수전공 이수 시 소속 학과와 복수전공 학과의 교과과정에서 전공과목으로 공통 
인정하는 과목 (단과대학 내에서 공통과목 포함)은 9학점까지, 소속 학과(부)와 
복수전공 학과(부)의 교과과정에서 공통 인정하는 타 학과(부)의 과목은 3학점까지 
전공학점의 이수로 중복하여 인정할 수 있다.
연합전공의 이수 시 소속 학과(부)에서 연합전공 교과과정에 제공한 과목은  
9학점까지, 소속 학과 (부)와 연합전공의 교과과정에서 공통으로 인정하는 타 학과 
(부) 과목은 3학점까지 전공학점의 이 수로 중복하여 인정할 수 있다.
부전공 이수 시에는 과목을 중복하여 인정하지 아니한다.  
단, 먼저 2과목까지는 선수로 듣는 것은 허용된다.


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단, 먼저 2과목까지는 선수로 듣는 것은 허용된다.
*** 미술대학 내에서 복수전공 시에는 1학년 기초 전필과목을 대상으로  
        총 9학점을 인정한다.
전공과목의 중복인정
4-7


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36 37
자리배정은 개강 직전인 2월말과 8월말 두 차례 전 학년이 모여 학기의 시작과 함께 
준비한다. 함께 쓰는 49동의 유지와 관리를 위한 것으로 반드시 참여해야 하는 
최소한의 수칙이다. 1학기는 2월말, 2학기는 8월말로 하되, 모이는 시간과 장소는 
과사무실에서 공지한다. 참여하지 않은 학생은 자리 배치, 실험실습기자재 지원 등의 
학과 혜택에서 불이익을 받을 수 있다.
건물 출입은 학생증을 통해 가능하며, 출입 등록이 안되어 있을 경우, 50동 
서무행정실에 방문하여 등록한다.
◦ 학기전 자리배정 및 실기실 안전교육 (년2회)
◦ 출입 등록
디자인연구동(49동)
5-1
장비의 대여는 원칙적으로 디자인전공 학생이 수업을 위해 사용하는 용도로만 대여가 
가능하다. 장비 대여시에는 반드시 학생증을 지참하고 학과사무실에 학생증을 맡기고, 
학과 장비 대여 장부에 기입한 후, 장비를 대여한다.


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학과 장비 대여 장부에 기입한 후, 장비를 대여한다.
장비는 최장 하루를 넘지 않도록 하며, 절대 교외로 반출되지 않도록 한다. 만약 반출 
시 적발 될 경우에는 차후 장비 대여에 대한 제한을 받을 수 있다. 장비 대여시 장비에 
이상이 없는지, 구성품을 확인하여 고장 유무를 확인한다. 분실 및 장비 훼손 시에는 
학생에게 상당의 책임을 물을 수 있다.
◦ 장비 대여 및 반납
디자인전공의 전시장은 49동 S&D홀, 215호, 215-1호와 220동 지하 전시실을 사용할 
수 있으며 그 밖에 전시 공간은 협의 하에 사용할 수 있다.
디자인연구동 전시장은 학기 중의 과제전, 졸업전, 공식적인 행사 등의 요청에 의해 
우선적으로 사용할 수 있으며, 그 밖의 개인 전시 등은 위의 일정을 피해 사용할 수 
있다. 전시장의 사용은 과사무실에 사용신청서를 제출하여 허가 후 사용한다.
전시장을 사용하고 난 후에는 청소 및 벽면 상태에 대한 검사를 맡아야 하며, 그에 따른


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전시장을 사용하고 난 후에는 청소 및 벽면 상태에 대한 검사를 맡아야 하며, 그에 따른 
청소 상태 불량에 대한 책임을 질 의무가 있다.
디자인연구동의 전산실 109호는 수업시간 이외에는 자유롭게 사용할 수 있으며 
야간 잔류 시에는 신청서를 작성한 후 사용한다. 전산실의 컴퓨터는 공용 컴퓨터로써 
불법소프트웨어의 설치가 금지되므로 각별한 주의를 요한다. 또한 여러 명이 공동으로 
사용하는 컴퓨터이므로 필요한 작업을 수행한 후에는 컴퓨터의 파일과 주변을 정리 
정돈 한다. 또한 전산실에는 중앙전산원에서 관리하는 흑백 A4프린터기가 있어서 
복사카드를 사용하여 출력이 가능하다.
전시장
전산실
5-2
5-3
5. 학사일반


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38 39
디자인연구동의 사진 스튜디오는 지하에 위치해 있으며, 학과사무실에서 미리 신청서 
작성, 보증금 3만원을 지급한 후, 키를 지급받아 사용할 수 있다. 관리상의 문제로 사진 
스튜디오는 디자인 전공 구성원 중 졸업전시 과목 준비에 관한 용무만 허가한다. 
사진 스튜디오의 사용 후 조교 및 근로장학생의 검사를 받는다. 만약 기물이 
고장나거나, 스튜디오 배경에 발자국 등 사용 상태가 불량하다고 판단되면 예치금은 
환급하지 않으며 기물 수리, 청소 등의 책임을 져야 한다.
사진 스튜디오
5-4
모형제작실을 사용할 시에는 안전과 직접적으로 관련된 안전수칙을 충분히 숙지하고 
안전수칙에 따라 작업한다. 실제로 위험한 기계를 사용하다가 부상을 당하는 경우가 
많으므로 작업시에 무엇보다 안전에 유의한다. 
※ 디자인학부 모형제작실 안전수칙
• 실습장내의 기계를 사용할 때에는 반드시 직원(트렌드아이디)의 입회 또는 감독     
   하에서만 사용할 수 있다.


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하에서만 사용할 수 있다.
• 실습장 내(內)에서는 반드시 단순한 작업복장으로 출입하여야 한다.
   (끈이 길게 달린 복장, 긴 모발 등은 안전사고를 유발할 있으므로 특히 주의한다.)
• 실습장내에서는 타인의 작업에 방해가 되지 않도록 하여야 한다.
• 각종 기계류를 사용할 때에는 반드시 규정된 안전수칙과 작업 요령을 숙지한 후   
   소중하게 사용하여야 한다.
• 각종 수공구를 사용한 후에는 반드시 지정된 장소에 가져다 놓아야 한다.
• 절대 화기 접근을 금한다.
• 작업이 모두 끝난 후에는 주변을 깨끗이 청소한다.
• 일과시간 외에 부득이하게 야간작업이 필요할 때는 수위실에 신고한 후 사용한다.
모형제작실
5-5


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40 41
학과의 실습실의 리스트는 아래와 같으며, 각 학년에 소속된 학과 실습실을 사용하는 
것을 원칙으로 한다. 학생들이 공동으로 사용하는 공간인만큼 책임감을 가지고 공간을 
정리하면서 사용하도록 한다. 
제2전공의 전공생도 학과 실습실의 공간을 사용할 수 있으며, 이 때 각과의 구성원들과 
합의 하에 자리 및 사물함을 배정받을 수 있다.
1학년
2학년
3학년 VD
3학년 ID
4학년 VD
4학년 ID
52동 402호
49동 112호
49동 210호
49동 212호
49동 211호
49동 213호
과방(학생실기실)
5-6
시기
1월초
2월중
2월중
3월
3월초
2월말
4월초
4월중
8월말
11월초
12월초
12월초
2월말
대상
동문, 교수, 졸업생
전공주임,조교,신입생
신입생,재학생,조교
신입생, 교수
신입생, 교수
학부생. 대학원생
학부생, 교수
학부생 전체, 교수
학부생. 대학원생
디자인전공 구성원 
귀빈,교수,졸업대상자
졸업대상자
졸업확정자
내용


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학부생. 대학원생
디자인전공 구성원 
귀빈,교수,졸업대상자
졸업대상자
졸업확정자
내용
학교생활의 기본이 되는 학사 및 
기타 정보를 교육
미술대학 신입생 전체 엠티
본부행사 / 체육관
미술대학전체 교수소개 / 미술관
자리배정 및 실기실 안전교육
학부 전공진입을 축하하는 자리
디자인전공 총엠티(전체참석)
자리배정 및 실기실 안전교육
디자인전공 개최 국제학술대회
졸업전시 전 귀빈 초청
졸업전시회 개최
본부행사 / 체육관
행사명
신년하례식
신입생오리엔테이션
새내기 새로배움터
입학식
미술대학 오리엔테이션
실기실 안전교육
전공진입식
총엠티(스케치여행)
실기실 안전교육
IDCC
졸업전시프리뷰
졸업전시
졸업식
6. 연간행사


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초안 | 이장섭 교수, 이경태 조교
수정 | 이장섭 교수, 이경태 조교
발행일 | 2024년 2월
주소 | 서울 관악구 관악로 1 서울대학교 49동 디자인연구동 101호
연락처 | 02.880.7512/7463


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Summary of ChatGPT/GPT-4 Research
and Perspective Towards the Future of Large
Language Models
Yiheng Liu ∗1, Tianle Han ∗1, Siyuan Ma 1, Jiayue Zhang 1,
Yuanyuan Yang1, Jiaming Tian 1, Hao He 1, Antong Li 2, Mengshen
He1, Zhengliang Liu 3, Zihao Wu 3, Dajiang Zhu 4, Xiang Li 5, Ning
Qiang1, Dingang Shen 6,7,8, Tianming Liu 3, and Bao Ge †1
1School of Physics and Information Technology, Shaanxi Normal University, Xi’an
710119 China


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710119 China
2School of Life and Technology Biomedical-Engineering, Xi’an Jiaotong University,
Xi’an 710049, China
3School of Computing, The University of Georgia, Athens 30602, USA
4Department of Computer Science and Engineering, The University of Texas at
Arlington, Arlington 76019, USA
5Department of Radiology, Massachusetts General Hospital and Harvard Medical
School, Boston 02115, USA
6School of Biomedical Engineering, ShanghaiTech University, Shanghai 201210,
China


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China
7Shanghai United Imaging Intelligence Co., Ltd., Shanghai 200230, China
8Shanghai Clinical Research and Trial Center, Shanghai 201210, China
Abstract
This paper presents a comprehensive survey of ChatGPT and GPT-4,
state-of-the-art large language models (LLM) from the GPT series, and
their prospective applications across diverse domains. Indeed, key innova-
tions such as large-scale pre-training that captures knowledge across the


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entire world wide web, instruction ﬁne-tuning and Reinforcement Learn-
ing from Human Feedback (RLHF) have played signiﬁcant roles in en-
hancing LLMs’ adaptability and performance. We performed an in-depth
analysis of 194 relevant papers on arXiv, encompassing trend analysis,
word cloud representation, and distribution analysis across various appli-
cation domains. The ﬁndings reveal a signiﬁcant and increasing interest
in ChatGPT/GPT-4 research, predominantly centered on direct natural


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language processing applications, while also demonstrating considerable
potential in areas ranging from education and history to mathematics,
∗Co-ﬁrst author
†Corresponding author: bob ge@snnu.edu.cn
1
arXiv:2304.01852v1  [cs.CL]  4 Apr 2023


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medicine, and physics. This study endeavors to furnish insights into Chat-
GPT’s capabilities, potential implications, ethical concerns, and oﬀer di-
rection for future advancements in this ﬁeld.
1 Introduction
Recent advances in natural language processing (NLP) have led to the devel-
opment of powerful language models such as the GPT (Generative Pre-trained
Transformer) series [1, 2, 3, 4, 5], including large language models (LLM) such


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as ChatGPT and GPT-4. These models are pre-trained on vast amounts of
text data and have demonstrated exceptional performance in a wide range of
NLP tasks, including language translation, text summarization, and question-
answering. In particular, the ChatGPT model has demonstrated its potential
in various ﬁelds, including education, healthcare, reasoning, text generation,
human-machine interaction, and scientiﬁc research.
A key milestone of LLM development is InstructGPT [5], a framework that


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allows for instruction ﬁne-tuning of a pre-trained language model based on Re-
inforcement Learning from Human Feedback (RLHF) [6, 5]. This framework
enables an LLM to adapt to a wide range of NLP tasks, making it highly ver-
satile and ﬂexible by leveraging human feedback. RLHF enables the model to
align with human preferences and human values, which signiﬁcantly improves
from large language models that are solely trained text corpora through unsu-


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pervised pre-training. ChatGPT is a successor to InstructGPT. Since its release
in December 2022, ChatGPT has been equipped with these advanced develop-
ments, leading to impressive performances in various downstream NLP tasks
such as reasoning and generalized text generation. These unprecedented NLP
capabilities spur applications in diverse domains such as education, healthcare,
human-machine interaction, medicine and scientiﬁc research. ChatGPT has re-


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ceived widespread attention and interest, leading to an increasing number of
applications and research that harness its exceeding potential. The open release
of the multi-modal GPT-4 model further expands the horizon of large language
models and empowers exciting developments that involve diverse data beyond
text.
The purpose of this paper is to provide a comprehensive survey of the existing
research on ChatGPT and its potential applications in various ﬁelds. To achieve


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this goal, we conducted a thorough analysis of papers related to ChatGPT in
the arXiv repository. As of April 1st, 2023, there are a total of 194 papers
mentioning ChatGPT on arXiv. In this study, we conducted a trend analysis of
these papers and generated a word cloud to visualize the commonly used terms.
Additionally, we also examined the distribution of the papers across various
ﬁelds and presented the corresponding statistics. Figure 1 displays the daily


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submission trend of papers related to ChatGPT, indicating a growing interest
in this ﬁeld. Figure 2 illustrates the word cloud analysis of all the papers. We
can observe that the current research is primarily focused on natural language
processing, but there is still signiﬁcant potential for research in other ﬁelds such
2


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Figure 2: Word cloud analysis of all the 194 papers.
as education and history. This is further supported by Figure 3, which displays
the distribution of submitted papers across various ﬁelds, highlighting the need
for more research and development in these areas.
This paper aims to shed light on the promising capabilities of ChatGPT
and provide insight into its potential impact in the future, including ethical
considerations. Through this survey, we hope to provide insights into how these


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models can be improved and extended in the future. In section 2, we will
review the existing work related to ChatGPT, including its applications, ethical
considerations, and evaluation. In addition to discussing the current state of
research related to ChatGPT, we will also explore its limitations in section 3.
Furthermore, we will provide guidance on future directions for language model
development.
2 Related work of ChatGPT


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development.
2 Related work of ChatGPT
In this section, we review the latest research related to the application, ethics,
and evaluation of ChatGPT.
2.1 Application of ChatGPT
2.1.1 Question And Answering
In the education ﬁeld
ChatGPT is commonly used for question and answers testing in the edu-
cation sector. Users can use ChatGPT to learn, compare and verify answers
for diﬀerent academic subjects such as physics, mathematics, and chemistry,
4


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and/or conceptual subjects such as philosophy and religion. Additionally, users
can ask open-ended and analytical questions to understand the capabilities of
ChatGPT.
In the ﬁeld of mathematics, Frieder et al. [7] constructed the GHOSTS nat-
ural language dataset, which consists of graduate-level math test questions.
The authors tested ChatGPT’s math abilities on the GHOSTS dataset us-
ing a question-and-answer format and evaluated it according to ﬁne-grained


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standards.In the Grad Text dataset, which covers simple set theory and logic
problems, ChatGPT performed the best. However, in the Olympiad-Problem-
Solving dataset, ChatGPT performed poorly, receiving only two 4-point scores
(out of a total of 5), with the majority of scores being 2 points. In the Holes-
in-Proofs dataset, ChatGPT received the lowest score of 1 point. In the MATH
dataset, ChatGPT only scored impressively in 26% of cases. These results sug-


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gest that ChatGPT’s math abilities are clearly lower than those of ordinary
math graduate students. Although ChatGPT can generally understand math
problems, it fails to provide the correct solutions. Pardos et al. [8] used the
Open Adaptive Tutoring system (OATutor) to investigate whether prompts gen-
erated by ChatGPT were helpful for learning algebra, with 77 participants from
Mechanical Turk taking part in the experiment. The experiment used ques-


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tions from OpenStax’s Elementary and Intermediate Algebra textbooks. These
participants were randomly assigned to either a control group (with manual
prompts) or an experimental group (with ChatGPT prompts). For each ques-
tion in both courses, the authors obtained answers from ChatGPT through a
question-and-answer format and evaluated scores according to three criteria:
ChatGPT provided an answer, the answer was correct, and inappropriate lan-


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guage was not used in the answer. The study found that 70% of prompts gener-
ated by ChatGPT passed manual quality checks, and both humans and Chat-
GPT produced positive learning gains. However, the scores of human prompts
ranged from 74.59% to 84.32%, signiﬁcantly higher than those of ChatGPT
prompts. Shakarian et al. [9] studied the performance of ChatGPT on math
word problems (MWPs), using the DRAW-1K dataset for experimentation. The


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dataset consists of 1000 MWPs and their answers, along with algebraic equa-
tion templates for solving such problems. The authors used the idea of machine
learning introspection and built performance prediction models using random
forests and XGBoost, and evaluated them on the dataset using ﬁve-fold cross-
validation. ChatGPT’s accuracy increased from an initial 34% to a ﬁnal 69%,
while its recall increased from an initial 41% to a ﬁnal 83%. The authors also


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found that ChatGPT’s failure rate decreased from an initial 84% to a ﬁnal
20%, indicating that performance can vary greatly depending on speciﬁc job
requirements.
In the ﬁeld of physics, Lehnert et al. [10] explored the capabilities and limita-
tions of ChatGPT by studying how it handles obscure physics topics such as the
swamp land conjecture in string theory. The experimental dialogue began with
broader and more general questions in the ﬁeld of string theory before narrowing


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down to speciﬁc swamp land conjectures and examining ChatGPT’s understand-
ing of them. The study found that ChatGPT could deﬁne and explain diﬀerent
6


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concepts in various styles, but was not eﬀective in truly connecting various con-
cepts. It would conﬁdently provide false information and fabricate statements
when necessary, indicating that ChatGPT cannot truly create new knowledge
or establish new connections. However, in terms of identifying analogies and
describing abstract concepts of visual representation, ChatGPT can cleverly
use language. Kortemeyer et al. [11] evaluated ChatGPT’s ability to answer


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calculus-based physics questions through a question-and-answer test. The tests
included online homework, clicker questions, programming exercises, and exams
covering classical mechanics, thermodynamics, electricity and magnetism, and
modern physics. While ChatGPT was able to pass the course, it also demon-
strated many misconceptions and errors commonly held by beginners. West et
al. [12] used the Force Concept Inventory (FCI) to evaluate ChatGPT’s accu-


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racy in answering physics concept problems related to kinematics and Newtonian
mechanics in the ﬁrst semester of college physics. The FCI covers topics such
as kinematics, projectile motion, free fall, circular motion, and Newton’s laws.
The study included data from 415 students who took the FCI at the end of the
semester, with an average score of 56%, while ChatGPT scored approximately
between 50% to 65%. The authors demonstrated that ChatGPT’s performance


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in physics learning can reach or even exceed the average level of a semester of
college physics.
In the medical ﬁeld
ChatGPT’s question-answering capabilities can also be applied in the med-
ical ﬁeld, such as for answering medical questions from patients or assisting
healthcare professionals in diagnosing diseases. Nov et al. [13] evaluated the fea-
sibility of using ChatGPT for patient-doctor communication. The experiment


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extracted 10 representative patient-doctor interactions from EHR, placed the
patient’s questions in ChatGPT, and asked ChatGPT to respond using roughly
the same number of words as the doctor’s response. Each patient’s question was
answered by either the doctor or ChatGPT, and the patient was informed that
5 were answered by the doctor and 5 were generated by ChatGPT, and was
asked to correctly identify the source of the response. The results of the exper-


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iment showed that the probability of correctly identifying ChatGPT’s response
was 65.5%, while the probability of correctly identifying the doctor’s response
was 65.1%. In addition, the experiment found that the patient’s response to
the trustworthiness of ChatGPT’s function was weakly positive (average Likert
score: 3.4), and trust decreased as the complexity of health-related tasks in
the questions increased. ChatGPT’s responses to patient questions were only


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slightly diﬀerent from those of doctors, but people seem to trust ChatGPT to
answer low-risk health questions, while for complex medical questions, people
still tend to trust the doctor’s responses and advice.
Tu et al. [14] explored the causal discovery ability of ChatGPT in the diag-
nosis of neuropathic pain. Causal relationship discovery aims to reveal potential
unknown causal relationships based purely on observed data [15]. The experi-


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knowledge and concepts beyond the existing textual training data corpus, that
is, it only understands language commonly used to describe situations and not
underlying knowledge. In addition, its performance consistency and stability are
not high, as the experiment observed that it would provide diﬀerent answers for
the same question under multiple inquiries. However, despite the many limita-
tions of ChatGPT, we believe that it has a great opportunity to improve causal
relationship research.


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relationship research.
In the other ﬁeld
Guo et al. [16] attempted to apply ChatGPT in the ﬁeld of communication,
speciﬁcally using ChatGPT for ordered importance semantic communication,
where ChatGPT plays the role of an intelligent consulting assistant that can
replace humans in identifying the semantic importance of words in messages and
can be directly embedded into the current communication system. For a message
to be transmitted, the sender ﬁrst utilizes ChatGPT to output the semantic


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importance order of each word. Then, the transmitter executes an unequal error
protection transmission strategy based on the importance order to make the
transmission of important words in the message more reliable. The experimental
results show that the error rate and semantic loss of important words measured
in the communication system embedded with ChatGPT are much lower than
those of existing communication schemes, indicating that ChatGPT can protect


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important words well and make semantic communication more reliable.
Wang et al. [17] studied the eﬀectiveness of ChatGPT in generating high-
quality Boolean queries for systematic literature search. They designed a wide
range of prompts and investigated these tasks on more than 100 systematic
review topics. In the end, queries generated by ChatGPT achieved higher accu-
racy compared to the currently most advanced query generation methods but


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at the cost of reduced recall. For time-limited rapid reviews, it is often accept-
able to trade oﬀ higher precision for lower recall. Additionally, ChatGPT can
generate high search accuracy Boolean queries by guiding the prompts. How-
ever, it should be noted that when two queries use the same prompts, ChatGPT
generates diﬀerent queries, indicating its limitations in consistency and stabil-
ity. Overall, this study demonstrated the potential of ChatGPT in generating


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eﬀective Boolean queries for systematic literature searches.
2.1.2 Text Classiﬁcation
The purpose of text classiﬁcation is to assign text data to predeﬁned categories.
This task is critical for many applications, including sentiment analysis, spam
detection, and topic modeling. While traditional machine learning algorithms
have been widely used for text classiﬁcation, recent advances in natural lan-
guage processing have led to the development of more advanced techniques.


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ChatGPT has shown immense potential in this ﬁeld. Its ability to accurately
classify text, ﬂexibility in handling various classiﬁcation tasks, and potential for
customization make it a valuable tool for text classiﬁcation, as evidenced by
several studies in the literature.
8


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Kuzman et al. [18] employed ChatGPT for automatic genre recognition, with
the goal of simplifying the text classiﬁcation task by utilizing ChatGPT’s zero-
shot classiﬁcation capability. They compared ChatGPT’s genre recognition per-
formance, using two prompt languages (EN and SL), with the X-GENRE clas-
siﬁer based on the multilingual model XLM-RoBERTa on the English dataset
EN-GINCO and the Slovenian dataset GINCO. The results showed that when


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EN was used as the prompt language, ChatGPT achieved Micro F1, Macro F1,
and Accuracy scores of 0.74, 0.66, and 0.72. However, on the GINCO dataset,
ChatGPT’s genre recognition performance with both EN and SL prompt lan-
guages was lower than that of the X-GENRE classiﬁer to varying degrees.
Amin et al. [19] evaluated the text classiﬁcation ability of ChatGPT in
aﬀective computing by using it to perform personality prediction, sentiment


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analysis, and suicide ideation detection tasks. They prompted ChatGPT with
corresponding prompts on three datasets: First Impressions, Sentiment140, and
Suicide and Depression, and compared its classiﬁcation performance with three
baseline models: RoBERTa-base, Word2Vec, and BoW. The results showed
that ChatGPT’s accuracy and UAR for the ﬁve personality classiﬁcations on
the First Impressions dataset were lower than the baseline methods to varying


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degrees. On the Sentiment140 dataset, ChatGPT’s accuracy and UAR were 85.5
and 85.5, respectively, which were better than the three baseline methods. On
the Suicide and Depression dataset, ChatGPT’s accuracy and UAR were 92.7
and 91.2, respectively, which were lower than RoBERTa, the best-performing
baseline method.
Zhang et al. [20] employed ChatGPT for stance detection, which includes
support and opposition. They used ChatGPT to classify the political stance


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of tweets in the SemEval-2016 and P-Stance datasets. SemEval-2016 contains
4,870 English tweets, and they selected tweets with the most commonly occur-
ring FM, LA, and HC political labels for stance classiﬁcation. The P-Stance
dataset has 21,574 English tweets, and they classiﬁed the stance of tweets to-
wards Trump, Biden, and Bernie. The ﬁnal results showed that on the SemEval-
2016 dataset, ChatGPT achieved F1-m scores of 68.4, 58.2, and 79.5 for the FM,


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LA, and HC political labels, and F1-avg scores of 72.6, 59.3, and 78.0, respec-
tively. On the P-Stance dataset, ChatGPT achieved F1-m scores of 82.8, 82.3,
and 79.4 for the Trump, Biden, and Bernie political ﬁgures, and F1-avg scores
of 83.2, 82.0, and 79.4, respectively.
Huang et al. [21] used ChatGPT to detect implicit hate speech in tweets.
They selected 12.5% (795 tweets) of the LatentHatred dataset containing im-
plicit hate speech and asked ChatGPT to classify them into three categories:


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implicit hate speech, non-hate speech, and uncertain. The results showed that
ChatGPT correctly recognized 636 (80%) of the tweets. The number of tweets
classiﬁed as non-hate speech and uncertain were 146 (18.4%) and 13 (1.6%),
respectively. The results of the reclassiﬁcation of tweets in the non-hate speech
and uncertain categories by Amazon Mechanical Turk (Mturk) workers were
consistent with ChatGPT’s classiﬁcation.
Overall, ChatGPT has tremendous potential in text classiﬁcation tasks, as it


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stance detection, and more. However, there are still challenges that ChatGPT
faces in the ﬁeld of text classiﬁcation. Firstly, it struggles to perform well in
classiﬁcation tasks with rare or out-of-vocabulary words since it heavily relies
on the distribution of training data. Additionally, the signiﬁcant computational
resources required for training and utilizing ChatGPT can limit its use in some
applications.
2.1.3 Text generation


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applications.
2.1.3 Text generation
We live in an era of information explosion, and text is an eﬃcient way of trans-
mitting information. The diversity of information has led to a diversity of text
categories. When researchers use ChatGPT’s text generation capabilities for
research, they inevitably choose to generate diﬀerent types of text. In the pro-
cess of reading papers, we found that the word count of the text generated by


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researchers increased from small to large, so we wanted to summarize existing
research based on the size of the text word count. We divided the generated
text into three levels: phrases, sentences, and paragraphs.
The following article uses ChatGPT to generate phrases. Zhang et al. [22]
proves that the semantic HAR model with semantic augmentation added dur-
ing training performs better in motion recognition than other models. Semantic


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augmentation requires shared tokens, which is lacking in some datasets. There-
fore, authors leverage ChatGPT for an automated label generation approach
for datasets originally without shared tokens. Fu et al. [23] describes a new
workﬂow for converting natural language commands into Bash commands. The
author uses ChatGPT to generate a candidate list of Bash commands based
on user input, and then uses a combination of heuristic and machine learning


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techniques to rank and select the most likely candidates. This workﬂow was
evaluated on a real command dataset and achieved high accuracy compared
to other state-of-the-art methods. Chen et al. [24] used the Bart model and
ChatGPT for the task of summarizing humorous titles and compared the perfor-
mance of the two models. It was found that the Bart model performed better on
large datasets, but ChatGPT was competitive with our best ﬁne-tuned model
in a small range (48), albeit slightly weaker.


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in a small range (48), albeit slightly weaker.
The following article uses ChatGPT to generate sentences.Chen et al. [25]
constructed a dialogue dataset (HPD) with scenes, timelines, character at-
tributes, and character relationships in order to use ChatGPT as a conver-
sational agent to generate dialogue. However, ChatGPT’s performance on the
test set was poor, and there is room for improvement.In study [26], chatGPT
demonstrated its ability to simplify complex text by providing three ﬁctional


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radiology reports to chatGPT for simpliﬁcation. Most radiologists found the
simpliﬁed reports to be accurate and complete, with no potential harm to pa-
tients. However, some errors, omissions of critical medical information and text
passages were identiﬁed, which could potentially lead to harmful conclusions if
not understood by the physicians. Xia et al. [27] proposes a new program re-
pair paradigm called Session-based Automatic Program Repair (APR). In APR,


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with validation feedback to construct the model’s input. The eﬀectiveness of
the approach is veriﬁed using the QuixBugs dataset. The experiment shows
that ChatGPT ﬁne-tuned with reinforcement learning from human feedback
(RLHF) outperforms Codex trained unsupervisedly in both repair datasets. In
reference to study [28], ChatGPT was compared to three commercial transla-
tion products: Google Translate2, DeepL Translate3, and Tencent TranSmart4.


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The evaluation was conducted on the Flores101 test set, using the WMT19
biomedical translation task to test translation robustness, with BLEU score as
the main metric. The study found that ChatGPT is competitive with commer-
cial translation products on high-resource European languages but falls behind
on low-resource or distant languages. The authors explored an interesting strat-
egy called pivot prompts, which signiﬁcantly improved translation performance.


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While ChatGPT did not perform as well as commercial systems on biomedical
abstracts or Reddit comments, it may be a good speech translator. Prieto et
al. [29] evaluated the use of ChatGPT in developing an automated construction
schedule based on natural language prompts. The experiment required building
new partitions in an existing space and providing details on the rooms to be
partitioned. The results showed that ChatGPT was able to generate a coher-


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ent schedule that followed a logical approach to meet the requirements of the
given scope. However, there were still several major ﬂaws that would limit the
use of this tool in real-world projects.Michail et al. [30] proposed a method
to improve the prediction accuracy of the HeFit ﬁne-tuned XLM T model on
tweet intimacy by generating a dataset of tweets with intimacy rating tags using
ChatGPT. The speciﬁc operation is to input tweets with intimacy rating tags


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into ChatGPT and then output similar tweets.
The following article uses ChatGPT to generate paragraphs. Wang et al.
[31] compared the abstract summarization performance of ChatGPT and other
models on various cross-lingual text datasets and found that ChatGPT may
perform worse in metrics such as R 1, R 2, R L, and B S. Yang et al. [32]
summarized the performance of ChatGPT in question answering-based text
summarization and found that, compared to ﬁne-tuned models, ChatGPT’s


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performance is slightly worse in all performance metrics. However, the article
suggests that if the dataset is golden annotation, ChatGPT’s performance may
surpass ﬁne-tuned models in these metrics. Belouadi et al. [33] compared the
ability of ByGPT5 and ChatGPT trained on a range of labeled and unlabeled
datasets of English and German poetry to generate constrained style poetry, and
evaluated them using three metrics: Rhyme, ScoreAlliteration, and ScoreMe-


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ter Score. The conclusion is that ByGPT5 performs better than ChatGPT.
Blanco-Gonzalez et al. [34] evaluated chatGPT’s ability to write commentary
articles, and in fact, this article itself was written by chatGPT. The human au-
thor rewrote the manuscript based on chatGPT’s draft. Experts found that it
can quickly generate and optimize text, as well as help users complete multiple
tasks. However, in terms of generating new content, it is not ideal. Ultimately,


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it can be said that without strong human intervention, chatGPT is not a use-
ful tool for writing reliable scientiﬁc texts. It lacks the knowledge and expertise
required to accurately and fully convey complex scientiﬁc concepts and informa-
11


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tion. Khalil et al. [35] on the originality of content generated by ChatGPT. To
evaluate the originality of 50 papers on various topics generated by ChatGPT,
two popular plagiarism detection tools, Turnitin and iThenticate, were used.
The results showed that ChatGPT has great potential in generating complex
text output that is not easily captured by plagiarism detection software. The
existing plagiarism detection software should update their plagiarism detection


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engines. Basic et al. [36] conducted a comparison of the writing performance
of students using or not using ChatGPT-3 as a writing aid. The experiment
consisted of two groups of 9 participants each. The control group wrote articles
using traditional methods, while the experimental group used ChatGPT as an
aid. Two teachers evaluated the papers. The study showed that the assistance of
ChatGPT did not necessarily improve the quality of the students’ essays.Noever


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et al. [37] discusses the potential of using artiﬁcial intelligence (AI), particularly
language models like GPT (including GPT-3), to create more convincing chat-
bots that can deceive humans into thinking they are interacting with another
person. The article describes a series of experiments in which they used GPT-3
to generate chatbot responses that mimic human-like conversations and were
tested on human participants. The results show that some participants were


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unable to distinguish between the chatbot and a real human, highlighting the
potential for these AI chatbots to be used for deceptive purposes.
2.1.4 Code Generation
Code generation refers to the process of automatically generating computer code
from high-level descriptions or speciﬁcations. ChatGPT’s advanced natural lan-
guage processing capabilities make it capable of performing code generation
tasks. By analyzing the requirements for code generation, ChatGPT can pro-


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duce code snippets that accurately execute the intended functionality. This not
only saves time and eﬀort in writing code from scratch but also reduces the risk
of errors that may occur during manual coding. In addition, ChatGPT’s ability
to learn and adapt to new programming languages and frameworks enables it
to complete more complex programming tasks. For example:
Megahed et al. [38] discussed the potential of using ChatGPT for tasks


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such as code explanation, suggesting alternative methods for problem-solving
with code, and translating code between programming languages. The solutions
provided by ChatGPT were found to be viable. In another study, Treude et
al. [39] introduced a ChatGPT-based prototype called GPTCOMCARE, which
helps programmers generate multiple solutions for a programming problem and
highlight the diﬀerences between each solution using colors.Sobania et al. [40]


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utilized ChatGPT for code bug ﬁxing, and further improved the success rate of
bug ﬁxing by inputting more information through its dialogue system. Speciﬁ-
cally, the QuixBugs standard bug ﬁxing benchmark contained 40 code bugs that
needed to be ﬁxed. With limited information, ChatGPT ﬁxed 19 bugs, which
was slightly lower than the 21 bugs ﬁxed by the Codex model, but signiﬁcantly
higher than the 7 ﬁxed by the Standard APR model. When given more prompts


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for code bug ﬁxing tasks.Xia et al. [27] proposed a conversational approach
for Automated Program Repair (APR), which alternates between generating
patches and validating them against feedback from test cases until the correct
patch is generated. Selecting 30 bugs from the QuixBugs standard bug ﬁxing
benchmark, which are suitable for test case feedback, and demonstrating them
with Java and Python, the QuixBugs-Python and QuixBugs-Java datasets were


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obtained. The conversational APR using ChatGPT outperformed the conversa-
tional APR using Codex and the conversational APR using CODEGEN (with
model parameters of 350M, 2B, 6B, and 16B) on both datasets. Furthermore,
ChatGPT’s conversational APR generated and validated patches with signiﬁ-
cantly fewer feedback loops than the other models.
ChatGPT can not only be used to achieve some simple code generation tasks
but also can be used to accomplish some complex programming tasks. Noever


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et al. [41] tested ChatGPT’s code generation capabilities on four datasets -
Iris, Titanic, Boston Housing, and Faker. When prompted to mimic a Python
interpreter in the form of a Jupyter notebook, the model was able to generate
independent code based on the prompt and respond with the expected out-
put. For example, when given the prompt ”data.cor()” for the Iris dataset,
ChatGPT generated correct Python output. The test results indicate that


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ChatGPT can access structured datasets and perform basic software opera-
tions required by databases, such as create, read, update, and delete (CRUD).
This suggests that cutting-edge language models like ChatGPT have the nec-
essary scale to tackle complex problems. McKee et al. [42] utilized ChatGPT
as an experimental platform to investigate cybersecurity issues. They modeled
ﬁve diﬀerent modes of computer virus properties, including self-replication, self-


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modiﬁcation, execution, evasion, and application, using ChatGPT. These ﬁve
modes encompassed thirteen encoding tasks from credential access to defense
evasion within the MITRE ATT&CK framework. The results showed that the
quality of ChatGPT’s generated code was generally above average, except for the
self-replication mode, where it performed poorly.They [43] also employed Chat-
GPT as a network honeypot to defend against attackers. By having ChatGPT


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mimic Linux, Mac, and Windows terminal commands and providing interfaces
for TeamViewer, nmap, and ping, a dynamic environment can be created to
adapt to attackers’ operations, and logs can be used to gain insight into their
attack methods, tactics, and procedures. The authors demonstrated ten hon-
eypot tasks to illustrate that ChatGPT’s interface not only provides suﬃcient
API memory to execute previous commands without defaulting to repetitive


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introductory tasks but also oﬀers a responsive welcome program that maintains
attackers’ interest in multiple queries.
In the ﬁeld of code generation, there are still several challenges with Chat-
GPT. Firstly, its application scope is limited as its training data is biased to-
wards programming languages such as Python, C++, and Java, making it po-
tentially unsuitable for some programming languages or coding styles. Secondly,


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manual optimization is necessary for code formatting, as the generated code may
not be performance-optimized or follow best coding practices, requiring manual
editing and optimization. Lastly, the quality of the generated code cannot be
13


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guaranteed, as it heavily relies on the quality of the natural language input,
which may contain errors, ambiguities, or inconsistencies, ultimately aﬀecting
the accuracy and reliability of the generated code.
2.1.5 Inference
Inference refers to the process of drawing new conclusions or information through
logical deduction from known facts or information. It is typically based on a se-
ries of premises or assumptions, and involves applying logical rules or reasoning


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methods to arrive at a conclusion. Inference is an important ability in human
thinking, and is often used to solve problems, make decisions, analyze and eval-
uate information, etc. Inference also plays a key role in ﬁelds such as science,
philosophy, law, etc. There are two types of inference: inductive reasoning,
which involves deriving general rules or conclusions from known facts or expe-
riences, and deductive reasoning, which involves deriving speciﬁc conclusions


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from known premises or assumptions. Whether inductive or deductive, the pro-
cess of inference requires following strict logical rules to ensure the correctness
and reliability of the inference.
Some papers attempt to use ChatGPT’s ability in inductive reasoning to
capture the meaning in text and use deﬁned metrics to score the text. Michail
et al. [30] uses ChatGPT to infer intimacy expressed in tweets. They ﬁrst input
50 tweets with intimacy markers to ChatGPT, then use inductive reasoning to


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infer the standards for generating tweets with diﬀerent levels of intimacy, and
ﬁnally generate ten tweets with intimacy values ranging from 0 to 5. Susnjak
et al. [44] collected a large amount of textual data from patient-doctor dis-
cussion forums, patient testimonials, social media platforms, medical journals,
and other scientiﬁc research publications. Using the BERT model, the author
inferred emotion values from 0 to 1. The author visualized the process of how


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the presence of bias in the discourse surrounding chronic manifestations of the
disease using the SHAP tool. The author also envisioned ChatGPT as a replace-
ment for the BERT model for scoring the emotional value of text. Huang et al.
[21] chose 12.5% of individuals in the potential hate dataset as study materials,
induced ChatGPT to make classiﬁcations based on a prompt, and ChatGPT
produced three classiﬁcations: unclear, yes, and no. The author assigned a


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value of 1 to yes, -1 to no, and 0 to unclear, and had ChatGPT score and clas-
sify them. ChatGPT was able to correctly classify 80% of implicit hate tweets
in the author’s experimental setup, demonstrating ChatGPT’s great potential
as a data labeling tool using simple prompts.
Some papers have evaluated ChatGPT’s reasoning performance, mainly in
decision-making and spatial reasoning, and identifying ambiguity. Tang et al.


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[45] used the independence axiom and the transitivity axiom, as well as other
non-VNM related decision-making abilities, by presenting bets conditioned on
random events, bets with asymmetric outcomes, decisions encapsulating Sav-
age’s Sure Thing principle, and other complex bet structures like nested bets, to
design experiments where each experiment input a short prompt to ChatGPT
and evaluated the results. The conclusion is that ChatGPT exhibits uncer-
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tainty in the decision-making process: in some cases, large language models
can arrive at the correct answer through incorrect reasoning; and it may make
suboptimal decisions for simple reasoning problems. Ortega-Martn et al. [46]
had ChatGPT detect three diﬀerent levels of language ambiguity and evaluated
its performance. The conclusion is that In semantics, ChatGPT performed per-
fectly in the detection of ambiguities. Apart from that, it has some bright sports


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(co-reference resolution) and some weaknesses (puts gender bias over grammar
in some non-ambiguous situations). In the generation task ChatGPT did well,
but also revealed some of its worse issues: the lack of systematicity. Lastly, it
should also be pointed that in most of the cases ChatGPT brilliantly alludes to
lack of context as the key factor in disambiguation.
2.1.6 Data or information extraction, transformation, enhancement,
processing
Data Visualization


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processing
Data Visualization
Natural language interfaces have contributed to generating visualizations
directly from natural language, but visualization problems remain challenging
due to the ambiguity of natural language.ChatGPT provides a new avenue for
the ﬁeld by converting natural language into visualized code.
In terms of data visualization, Noever et al. [41] tested ChatGPT’s ba-
sic arithmetic skills by asking questions.On the iris dataset, Titanic survival


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dataset, Boston housing data, and randomly generated insurance claims dataset,
the statistical analysis of data and visualization problems were converted to
programming problems using Jupyter to verify ChatGPT’s ability to generate
python code to draw suitable graphs and analyze the data. The results show
that ChatGPT can access structured and organized datasets to perform the
four basic software operations required for databases: create, read, update, and


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delete, and generate suitable python code to plot graphs for descriptive statis-
tics, variable correlation analysis, describing trends, and other data analysis
operations.Maddigan et al. [47] proposed an end-to-end solution for visualizing
data in natural language using LLM, which uses an open-source python frame-
work designed to generate appropriate hints for selected datasets to make LLM
more eﬀective in understanding natural language, and uses internal reasoning


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capabilities to select the appropriate visualization type to generate the code for
visualization. In this paper,the reseachers compare the visualization results of
GPT-3, Codex and ChatGPT in the case of nvBench SQLite database [48] and
the visualization results of energy production dataset in the study of ADVISor
with NL4DV [49, 50].In addition to, they explore the ability to reason and hy-
pothesize of the LLM on movie dataset [48] when the hints are insuﬃcient or


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wrong .Experimental results show that LLM can eﬀectively support the end-to-
end generation of visualization results from natural language when supported
by hints, providing an eﬃcient, reliable and accurate solution to the natural
language visualization problem.
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Information Extraction
The goal of information extraction is to extract speciﬁc information from
natural language text for structured representation, including three important
subtasks such as entity relationship extraction, named entity recognition, and
event extraction, which have wide applications in business, medical, and other
ﬁelds.
In information extraction, Wei et al. [51] proposed ChatIE, a ChatGPT-
based multi-round question-and-answer framework for information extraction.


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The framework decomposes a complex information extraction (IE) task into
several parts, then combines the results of each round into a ﬁnal structured
result. The entity association triple extraction, named entity recognition, and
event extraction tasks were performed on six datasets NYT11-HRL, DuIE2.0
, conllpp, MSR , DuEE1.0 [52, 53, 54, 55, 56], and ACE05 in both languages,
comparing three metrics of precision, recall, and F1 score.These results sug-


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gest that on six widely used IE datasets, ChatIE improves performance by an
average of 18.98% compared to the original ChatGPT without ChatIE, and out-
performs the supervised models FCM and MultiR [57, 58] on the NYT11-HRL
dataset.While the original ChatGPT cannot solve complex IE problems with
original task instructions, and with this framework, successfully IE tasks were
implemented on six datasets.Gao et al. [59] explored the feasibility and chal-


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lenges of ChatGPT for event extraction on the ACE2005 corpus, evaluating the
performance of ChatGPT in long-tail and complex scenarios (texts containing
multiple events) and comparing it with two task-speciﬁc models, Text2Event
and EEQA [60, 61].Then,they explored the impact of diﬀerent cues on perfor-
mance of ChatGPT. The results show that the average performance of Chat-
GPT in long-tail and complex scenarios is only 51.04% of that of task-speciﬁc


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models such as EEQA. Continuous reﬁnement of cues does not lead to consis-
tent performance improvements, and ChatGPT is highly sensitive to diﬀerent
cue styles.Tang et al. [62] proposed a new training paradigm that incorporates
appropriate cues to guide ChatGPT to generate a variety of examples with dif-
ferent sentence structures and language patterns and eliminate the resulting
low-quality or duplicate samples for downstream tasks. Although compared to


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a soft model for a speciﬁc healthcare tasks, ChatGPT underperforms in Named
Entity Recognition (NER) and Relationship Extraction (RE) tasks , in the Gene
Association Database (GAD) Release; EU-ADR corpus for the RE task , the
innovative training framework was able to train local models, with F1 scores im-
proving from 23.37% to 63.99% for the named entity recognition task and from
75%, while alleviating privacy concerns and time-consuming data collection and


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annotation problems.He et al. [63] proposed a contextual learning framework
ICL- D3IE. this framework introduces formatted presentation, continuously it-
erates to update and improve the presentation, and then combines ChatGPT
for text information extraction. In the paper, ICL-D3IE is compared with ex-
isting pre-trained models such as LiLT,BROS (in-distribution (ID) setting and
out-of-distribution (OOD) setting) on datasets (FUNSD, CORD, and SROIE


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[64, 65, 66]).These results show that the ICL-D3IE method in all datasets and
settings except for the ID setting on CORD are superior to other methods,
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with ICL-D3IE (GPT-3) F1 scores reaching 90.32% on FUNSD and97.88% on
SROIE; in the out-of-distribution (OOD) setting, ICL-D3IE performs much
better than previous pre-trained methods on all datasets.Polak et al. [67] pro-
posed ChatExtract method - consisting of a set of engineering prompts applied
to a conversational LLM - for automatic data extraction. In the experiment,
they extracted a large number of sentences from hundreds of papers and ran-


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domly selected 100 sentences containing data and 100 sentences without data
as test data. The results show that the accuracy and recall of LLM exceeded
90% and may be comparable to human accuracy in many cases; in addition to
this, the experiments were conducted under the condition of removing follow-up
prompts and not keeping the conversation compared to previous experiments,
respectively. The accuracy of deleting follow-up questions dropped to 80.2%


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and the recall rate dropped to 88.0%. Removing the conversational aspect and
related information retention recall and accuracy dropped to 90.0% and 56.6%,
respectively, demonstrating the eﬀect of information retention combined with
purposeful redundancy on LLM information extraction performance.
Quality Assessment
For translation quality, text generation quality, manual assessment is usually
eﬀective but suﬀers from subjectivity and time-consuming, etc. It was found


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through exploration that ChatGPT has also achieved signiﬁcant performance
in automatic quality assessment.
In terms of quality assessment,Kocmi et al. [68] proposed a GPT-based
translation quality assessment metric, GEMBA, which evaluates the transla-
tion of each fragment individually and then averages all the obtained scores to
obtain a ﬁnal system-level score. In the MQM2022 test set (English-German,
English-Russian, and Chinese-English) [69], a scoring task was performed with


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a classiﬁcation task to compare the accuracy [70] and kendall tau scores [71] of
seven GPT models under four cue templates.The results showed that GEMBA
had the highest system-level accuracy of 88.0% compared to more than 10 au-
tomatic metrics such as BLEU, and among the seven GPT models, ChatGPT
accuracy is above 80%, in addition to, the best performance can be obtained in
the least constrained template, demonstrating the potential of LLM for trans-


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lation quality assessment tasks, but the evaluation is only applicable at the
system level and needs further improvement.Wang et al. [72] used ChatGPT
as a natural language generation (NLG) evaluator to study the correlation with
human judgment. On three datasets covering diﬀerent NLG tasks, task- and
aspect-speciﬁc cues were designed to guide ChatGPT for NLG evaluation in
CNN/DM [73], OpenMEVA-ROC, and BAGEL for summary, story generation,


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and data-to-text scoring, respectively. Then,they compute Spearman coeﬃ-
cients [74],Pearson correlation coeﬃcients [75]. Kendall’s Tau score [76] to as-
sess the correlation with human evaluations.The results show that ChatGPT is
highly correlated with human judgments in all aspects, with correlation coeﬃ-
cients of 0.4 or more in all categories, showing its potential as an NLG indicator.
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Data Augmentation
In natural language processing, text data augmentation is an eﬀective mea-
sure to alleviate the problem of low data quantity and low quality training data,
and ChatGPT has shown great potential in this regard.
In terms of data augmentation, Dai et al. [77] proposed a ChatGPT-based
text data augmentation method that reformulates each sentence in the train-
ing sample into multiple conceptually similar but semantically diﬀerent samples


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for classiﬁcation tasks downstream of the Bert model.On text transcriptions
and PubMed 20k datasets containing more than 8 hours of audio data of com-
mon medical symptom descriptions,experiments were conducted to compare co-
sine similarity and TransRate metrics with multiple data enhancement methods
[9].This paper shows that compared with existing data enhancement methods,
the proposed ChatAug method shows a double-digit improvement in sentence


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classiﬁcation accuracy and generates more diverse augmented samples while
maintaining its accuracy, but the original model is not ﬁne-tuned in the pa-
per and suﬀers from a lack of domain knowledge, which may produce incorrect
augmented data.
Multimodal fusion
ChatGPT can currently only process natural language directly, but with a
cross-modal encoder, it can combine natural language with cross-modal pro-
cessing to provide solutions for intelligent transportation, healthcare, and other
ﬁelds.


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ﬁelds.
In terms of multimodal data processing, Wu et al. [78] constructed a frame-
work that Visual ChatGPT integrates with diﬀerent Visual Foundation Models
(VFMs) and then combines a series of hints to input visual information to Chat-
GPT to solve visual problems.The paper shows examples of visual tasks such
as removing or replacing certain objects from images, interconversion between
images and text, demonstrating the Visual ChatGPT has great potential and


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capability for diﬀerent tasks.But there are issues during the task that require
a large number of hints to convert VFMs to language, invoke multiple VFMs
to solve complex problems leading to limited real-time capability, and security
and privacy issues. Zheng et al. [79] showed a text mining example of LLM
for extracting self-driving car crash data from California crash news, analyz-
ing a failure report example, and generating a crash report example based on


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keywords; introduced a use case concept of a smartphone-based framework for
automatic LLM failure report generation, which absorbs multiple data sources
captured by cell phone sensors and then transfers the data to a language space
for text mining, inference and generation, and further outputs the key informa-
tion needed to form a comprehensive fault report, demonstrating the potential
of LLM for a variety of transportation tasks.


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of LLM for a variety of transportation tasks.
Nowadays, ChatGPT shows a wide range of applications in data visualiza-
tion, information extraction, data enhancement, quality assessment, and multi-
modal data processing.But there are also issues on how to further utilize hints
to eﬀectively interact with ChatGPT, lack of ability to process and analyze data
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from devices such as sensors, and data privacy and security.
Cueing Techniques
Cue engineering provides important support for eﬀective dialogue with large
language models.White et al. [80] proposed a framework for cueing models
applicable to diﬀerent domains. This framework structures cues to interact
with LLMs by providing speciﬁc rules and guidelines. Also, this paper presents
a catalog of cueing patterns that have been applied to LLM interactions, as well


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as speciﬁc examples with and without cues. The advantages of the combinability
of prompting patterns are demonstrated, allowing users to interact with LLM
more eﬀectively, but patterns for reusable solutions and new ways to use LLM
need to be continuously explored.
2.1.7 Human-ChatGPT Collaboration
Collaboration between humans and machines is a process where humans and
machines work together to achieve a common goal. In such collaboration, hu-


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mans provide domain expertise, creativity, and decision-making abilities, while
machines provide automation, scalability, and computing power. ChatGPT is
an advanced natural language processing model that can understand and gen-
erate human-like language, thereby reducing communication costs. Its ability
to process and generate natural language makes it an ideal partner for human
collaboration. ChatGPT can oﬀer relevant suggestions, complete tasks based


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on human input, and enhance human productivity and creativity. It can learn
from human feedback and adapt to new tasks and domains, further improv-
ing its performance in human-machine collaboration. ChatGPT’s capability to
comprehend natural language and produce appropriate responses makes it a
valuable tool for various collaboration applications, as demonstrated by several
studies in the literature we have gathered.
Ahmad et al. [81] proposed a method for human-machine collaboration us-


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ing ChatGPT to create software architecture. This method transforms software
stories (created by software architects based on application scenarios) into feasi-
ble software architecture diagrams through continuous interaction between the
software architect and ChatGPT. During the evaluation stage, ChatGPT uses
the Software Architecture Analysis Method (SAAM) to evaluate each compo-
nent in the software architecture and generate evaluation reports. This method


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eﬃciently utilizes the knowledge and supervision of the architect with the ca-
pabilities of ChatGPT to collaboratively build software-intensive systems and
services. Lanzi et al. [82] proposed a collaborative design framework that com-
bines interactive evolution and ChatGPT to simulate typical human design pro-
cesses. Humans collaborate with large language models (such as ChatGPT) to
recombine and transform ideas, and use genetic algorithms to iterate through


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complex creative tasks. The results of three game design tasks showed that the
framework received positive feedback from game designers. The framework has
good reusability and can be applied to any design task that can be described in
free text form.
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In the future, ChatGPT’s ability to understand nonverbal cues such as tone
of voice and body language can be enhanced, enabling it to better understand
human thoughts and interact with people more eﬀectively.
2.1.8 ChatGPT Integration
Integration refers to combining diﬀerent systems or software components to
achieve a common goal. ChatGPT can be integrated as a part of a whole or
act as an integration tool to enable seamless communication between diﬀerent


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systems. Its natural language processing ability makes it easier for non-technical
users to interact with systems, reducing the need for specialized knowledge or
training. Some studies in the literature we collected have already demonstrated
this.
Treude et al. [39] integrated ChatGPT into the prototype of ”GPTCOM-
CARE” to address programming query problems. This integration allowed for
the generation of multiple source code solutions for the same query, which in-


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creased the eﬃciency of software development. The results of their study demon-
strated the eﬀectiveness of using ChatGPT to improve the quality and diversity
of code solutions, ultimately reducing the amount of time and eﬀort required for
software development.Wang et al. [83] proposed the chatCAD method, which
utilizes large language models (LLMs) such as ChatGPT to enhance the out-
put of multiple CAD networks for medical images, including diagnosis, lesion


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segmentation, and report generation networks. The method generates sugges-
tions in the form of a chat dialogue. The authors tested the eﬀectiveness of
the method on a randomly selected set of 300 cases from the MIMIC-CXR
dataset, which included 50 cases each of cardiomegaly, edema, consolidation,
atelectasis, pleural eﬀusion, and no ﬁndings. Compared to CvT2DistilGPT2
and R2GenCMN, chatCAD showed signiﬁcant advantages in RC and F1, while
only performing weaker than R2GenCMN in PR.


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only performing weaker than R2GenCMN in PR.
Integrating ChatGPT into applications will still present challenges. Firstly,
ChatGPT’s performance may be aﬀected by language barriers or diﬀerences
in terminology between diﬀerent systems. Additionally, ChatGPT’s responses
are not always deterministic, which poses a challenge when integrating with
systems that require precise and reproducible results. Finally, the processing


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time of ChatGPT is slow for integration tasks involving time-sensitive data such
as traﬃc, which is a limitation in time-critical environments.
2.2 AI Ethics
Since the advent of ChatGPT, this powerful natural language processing model
has not only brought great convenience to people but also triggered more crisis-
aware thinking. Some researchers have started to hypothesize and study the
potential negative impacts of ChatGPT. This proactive research provides good


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proposals for standardized construction to address future AI abuse issues.
Regarding the evaluation of ChatGPT’s own political and ethical tendencies,
Hartmann et al. [84] used Wahl-O-Mat, one of the most commonly used voting
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advice applications in the world, to show ChatGPT political statements from
diﬀerent parties, forcing it to make choices of agree, disagree, or neutral. The
results indicated that ChatGPT has a pro-environment, left-wing liberal ide-
ology, which was also conﬁrmed in the nation-state agnostic political compass
test. Another study (referenced as [85]) examined ChatGPT’s moral standards
by repeatedly asking it diﬀerent versions of the trolley problem, and found that


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ChatGPT gave answers with diﬀerent moral orientations, lacking a ﬁrm moral
stance. A subsequent test also found that ChatGPT’s lack of consistency could
aﬀect people’s moral judgments. Additionally, Borji et al. [86] demonstrated
ChatGPT’s inconsistency in reasoning, factual errors, mathematics, coding, and
bias across eleven related aspects. These ﬁndings highlight ChatGPT’s inher-
ent traits and limitations, and people should be aware of their potential impact


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when seeking advice from ChatGPT.
Regarding relevant policies and regulations, Hacker et al. [87] discussed the
nature and rules of large generative AI models, including ChatGPT, which are
rapidly changing the way we communicate, explain, and create. The author
suggested that diﬀerent stakeholders in the value chain should take regulatory
responsibility and deploy four strategies to tailor more comprehensive laws for


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the beneﬁt of society. Another study (referenced as [88]) criticized the European
Commission’s proposal on AI responsibility and suggested revising the proposed
AI responsibility framework to ensure eﬀective compensation while promoting
innovation, legal certainty, and sustainable AI regulation. A policy framework
was proposed (referenced as [89]) to customize LLMs, such as ChatGPT, in a so-
cially acceptable and safe manner, emphasizing the need to align large language


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models (LLMs) with human preferences.
The political and ethical tendencies of ChatGPT could inﬂuence users’ be-
havior and decision-making to some extent. However, some studies have con-
ducted in-depth research on the use of norms and limitations, which could enable
humans to use ChatGPT more reasonably and safely.
2.3 Evaluation
2.3.1 Comparison of ChatGPT with existing popular models
We use publicly available datasets to comprehensively evaluate the strengths


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and limitations of ChatGPT. Reference [90] evaluates the technical performance
of ChatGPT in multitask, multilingual, and multimodal aspects based on 23
standard public datasets and newly designed multimodal datasets, including
eight diﬀerent common natural language processing application tasks. The ex-
perimental results show that, in terms of multitasking, ChatGPT outperforms
various state-of-the-art zero-shot learning large language models in most tasks,


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and even outperforms ﬁne-tuned task-speciﬁc models in some individual tasks.
In terms of multilingualism, we found that ChatGPT cannot be applied to low-
resource languages because it cannot understand the language and generate
translations for that language. In terms of multimodality, ChatGPT’s ability is
still basic compared to specialized language-visual models.
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In terms of stability, reference [91] concludes that ChatGPT’s performance is
always lower than SOTA, the current state-of-the-art model, in almost all tasks.
This means that as a general model, ChatGPT has never reached the level of the
best existing models. Experimental data shows that the average quality of the
SOTA model is 73.7%, while the average quality of the ChatGPT model is only
56.5%. At the same time, ChatGPT’s stability is poor: the standard deviation


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of its performance is 23.3%, while the SOTA model’s standard deviation is
only 16.7%. This non-deterministic behavior exhibited by ChatGPT could be
a serious drawback in some problems.
Similarly, Qin et al. [92] conducted a comprehensive evaluation of whether
ChatGPT is a qualiﬁed general natural language processing task solver. The ex-
periment analyzed ChatGPT’s zero-shot learning ability based on 20 commonly
used public datasets covering 7 representative task categories. Below, we will


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analyze ChatGPT’s performance on each task:
In terms of reasoning tasks, ChatGPT performs average on mathematical
symbol, commonsense causal, and logical reasoning tasks, but performs well
in arithmetic reasoning [92]. That is to say, ChatGPT’s abilities vary among
diﬀerent types of reasoning tasks. In terms of logical reasoning, ChatGPT’s
deductive and abductive reasoning are superior to inductive reasoning, while
in other reasoning tasks, such as analogy, causal and commonsense reasoning,


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ChatGPT performs well [90].
In terms of sentiment analysis task, ChatGPT performs similarly to GPT-3.5
and bert-style models [92, 93]. However, according to literature [91], ChatGPT
has losses not exceeding 25% on most tasks, except for three relatively sub-
jective emotion perception tasks where it performs poorly. If we remove these
tasks to calculate the average quality of the two models, we ﬁnd that the SOTA
method has an average quality of 80%, while the ChatGPT method has an av-


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erage quality of 69.7%. That is to say, ChatGPT performs well on all tasks
except for emotion-related tasks, and can handle most of the problems we con-
sider. However, overall, its performance is lower than the SOTA model based
on experimental data, but the diﬀerence between the two is not very large.
In other tasks, according to literature [92], ChatGPT performs well in nat-
ural language inference, i.e., the task of inferring sentence relationships, and its


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performance on this task is signiﬁcantly better than all bert-style models [93].
However, while ChatGPT performs well on inference tasks, it may produce some
self-contradictory or unreasonable responses, which is its potential limitation.
In question-answering, dialogue, and summarization tasks, ChatGPT performs
better than the GPT-3.5 model [92], especially in the question-answering task,
where its performance is comparable to bert-style models [93]. Therefore, we


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have demonstrated that ChatGPT is a qualiﬁed general-purpose model.
However, ChatGPT also has limitations in many aspects. Firstly, it lacks the
ability to handle non-textual semantic reasoning tasks such as mathematical,
temporal, and spatial reasoning, and it performs poorly in multi-hop reasoning
[90]. Secondly, ChatGPT is not good at solving named entity recognition tasks
[92]. Furthermore, ChatGPT performs poorly in handling tasks involving nega-


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that, like other large pre-trained language models, ChatGPT has limitations in
completing complex reasoning tasks.
In summary, ChatGPT’s zero-shot performance is comparable to ﬁne-tuned
bert and GPT-3.5 models, and with the help of advanced prompting strategies,
ChatGPT can demonstrate better comprehension abilities. However, it still
cannot outperform the current SOTA models.
2.3.2 The possibility of using ChatGPT for plagiarism and cheating


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In response to the possibility of ChatGPT being used for plagiarism and cheat-
ing, Zhou et al. [94] reﬂected on the current state of development of artiﬁcial in-
telligence like ChatGPT. As ChatGPT becomes increasingly easy to obtain and
scalable in text generation, there is a high likelihood that these technologies will
be used for plagiarism, including scientiﬁc literature and news sources, posing
a great threat to the credibility of various forms of news media and academic


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articles. Some scholars are concerned that the end of paper as a meaningful
evaluation tool may be approaching [95, 96], as ChatGPT can easily generate
persuasive paragraphs, chapters, and papers on any given topic. Additionally,
it will exacerbate plagiarism issues in many ﬁelds such as education, medicine,
and law [10], and may be used for cheating in academic exams [97]. Deﬁnitional
recognition technology is a relatively eﬀective method for detecting plagiarism,


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and the deﬁnitional typology proposed in [94] can alleviate people’s concerns by
being used to construct new datasets. Susnjak [97] proposed a solution to the
possibility of large language models like ChatGPT being used for exam cheat-
ing: guiding ChatGPT to generate some critical thinking problems through
questioning, then providing answers and critically evaluating them. Analysis of
ChatGPT shows that it exhibits critical thinking, can generate highly realistic


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text in terms of accuracy, relevance, depth, breadth, logic, persuasiveness, and
originality. Therefore, educators must be aware of the possibility of ChatGPT
being used for exam cheating and take measures to combat cheating behavior
to ensure the fairness of online exams.
2.3.3 Feedback from ChatGPT users
In response to feedback from ChatGPT users, Haque et al. [98] conducted a
mixed-methods study using 10,732 early ChatGPT user tweets. The authors


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extracted Twitter data using Python and Twitter API and constructed the
ChatGPTTweet dataset, which contains 18k tweets. For each tweet, the au-
thors collected information on text content, user location, occupation, veriﬁca-
tion status, date of publication, and tags. Based on this dataset, the authors
studied the characteristics of early ChatGPT users, discussion topics related to
ChatGPT on Twitter, and the sentiment of Twitter users toward ChatGPT.


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For RQ1, the authors found that early ChatGPT users had a diverse and wide
range of occupational backgrounds and geographical locations. For RQ2, the
authors identiﬁed nine topics related to ChatGPT, including its impact on soft-
ware development, entertainment and creativity, natural language processing,
23


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education, chatbot intelligence, business development, search engines, question-
answering tests, and future careers and opportunities. For RQ3, most early
users expressed positive sentiment toward topics such as software development
and creativity, while only a few expressed concern about the potential misuse
of ChatGPT.
2.3.4 Adverse eﬀects of ChatGPT on users
Regarding the negative eﬀects of ChatGPT on users, Luan et al. [99] studied


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the psychological principles of ChatGPT, delved into the factors that attract
users’ attention, and revealed the impact of these factors on future learning.
In the post-pandemic era, teachers and students are both facing uncertainty
in the teaching process and job pressures. Under these common constraints of
education and employment, educators and students must re-evaluate current
educational methods and outcomes, as well as students’ future career devel-


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opment. Through question-and-answer exchanges with ChatGPT, people can
easily obtain appropriate solutions or key information, thereby enhancing their
motivation, eliminating anxiety in learning, improving interest, and achieving
psychological satisfaction. Subhash et al. [100] explored whether large language
models have the ability to reverse user preferences. With the development of
pre-trained large language models, people are increasingly concerned about the


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ability of these models to inﬂuence, persuade, and potentially manipulate user
preferences in extreme cases. Therefore, the literature [100] roughly qualita-
tively analyzed that adversarial behavior does lead to potential changes in user
preferences and behaviors in dialogue systems. If we want to further quanti-
tatively analyze the ability of large language models in this regard, additional
statistical summary techniques need to be used for future research.
3 Discussion
3.1 Limitations


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3 Discussion
3.1 Limitations
Despite the remarkable capabilities of ChatGPT and GPT-4, it still faces certain
limitations. Some of these limitations include:
Outdated Knowledge
The current models are trained on historical data (up to 2021), thereby
lacking real-time comprehension of current aﬀairs. This is a critical concern
in today’s information-explosion era, as the reliability of prior knowledge bases
progressively diminishes, potentially yielding inaccurate responses, especially in


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rapidly evolving domains such as jurisprudence and technology. Additionally,
these models are incapable of fact-checking while the training data is composed
of content from various sources, some of which may be unreliable, which may
result in seemingly plausible yet nonsensical responses.
24


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Insuﬃcient Understanding
While these models can interpret the majority of inquiries and contextual
situations, they occasionally encounter comprehension biases when addressing
ambiguous or contextually complex queries. Furthermore, in certain specialized
ﬁelds, the abundance of unique abbreviation exacerbates the models’ under-
standing challenges, resulting in incorrect and vacuous responses.
Energy Consumption
Throughout the training and inference stages, these large-scale models re-


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quire signiﬁcant computational resources and electrical power, resulting in ele-
vated energy consumption and signiﬁcant carbon emissions. Consequently, this
restricts their deployment and practical applications.
Malicious Usage
Despite OpenAI implementing a series of restrictions to mitigate model tox-
icity, instances of users evading these constraints through meticulously designed
prompts have emerged, inducing the model to produce unhealthy content or


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even using it for illicit commercial purposes.
Bias and Discrimination
Due to the inﬂuence of pre-training data, the models exhibit biases in polit-
ical, ideological, and other areas. The application of LLMs in public domains,
such as education and publicity, should be approached with extreme caution.
Privacy and Data Security
Concurrent with the expansion of users, protecting user privacy and data
security becomes increasingly important. In fact, ChatGPT was banned in


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Italy in early April due to privacy concerns. This is particularly relevant given
the models’ extensive collection of personal information and preferences during
interactions, and as future multimodal models, such as GPT-4, may frequently
require users to upload private photos.
3.2 Future Directions
In forthcoming research, the development of models based on ChatGPT and
GPT-4 may focus on addressing these limitations to enhance their practical
applications.


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applications.
Primarily, researchers should continue to work on reﬁning model training
methodologies while ﬁltering pre-training data to minimize the presence of mis-
leading information in the model’s knowledge base, thereby obtaining accurate
responses. Concurrently, it is crucial to emphasize training approaches that
economize computational resources, thereby mitigating costs and broadening
potential application scenarios.


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potential application scenarios.
Moreover, the advancements in context-awareness and disambiguation tech-
nologies are anticipated to facilitate enhanced comprehension of complex queries
25


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by models, improving the accuracy, relevance, and context-awareness of AI-
generated content. Integrating real-time data streams can also keep these mod-
els in sync with current events and trends, enabling them to provide up-to-date
information such as live traﬃc, weather, and stock updates.
Additionally, developers should engage in interdisciplinary collaboration with
specialists from diverse domains, including policy-making, jurisprudence, and


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sociology, with the objective of formulating standard and ethical frameworks
for LLM development, deployment, and utilization, thereby alleviating poten-
tial harmful consequences. In terms of public awareness and education, manda-
tory awareness training should be implemented prior to large-scale public de-
ployment and application to increase public awareness of LLM capabilities and
limitations while promoting responsible and informed utilization, especially in


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industries such as K-12 education and journalism.
Finally, the inﬂuence of ChatGPT and GPT-4 should not be limited to
just the NLP ﬁeld. They also show promising prospects in the areas of com-
puter vision, brain-inspired AI, and robotics. These models exhibit a capacity
for learning and comprehension comparable with human-level intelligence, po-
sitioning them as a pivotal component in the development of artiﬁcial general


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intelligence (AGI)[101]. Their ability to facilitate seamless interactions between
humans and robots paves the way for the execution of more complex tasks.
The remarkable capacity of zero-shot in-context learning of these models en-
ables quick adaptation to new tasks without the requirement for labeled data
for ﬁne-tuning, which is a critical challenge in ﬁelds like medical informatics[102]
and robotics[103] where the availability of labeled data is commonly limited or
non-existent.


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non-existent.
4 Conclusion
This review paper provides a comprehensive survey of ChatGPT and GPT-4,
highlighting their potential applications and signiﬁcant contributions to the ﬁeld
of natural language processing. The ﬁndings of this study reveal that the interest
in these models is growing rapidly, and they have shown considerable potential
for application across a wide range of domains. One key factor contributing
to the success of ChatGPT and GPT-4 is their ability to perform large-scale


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pre-training, which captures knowledge from the vast expanse of the internet,
allowing the models to learn from a massive amount of data. The integration of
Reinforcement Learning from Human Feedback (RLHF) has further enhanced
the model’s adaptability and performance, making it highly eﬃcient in process-
ing natural language. This study has also identiﬁed several potential ethical
concerns related to the development and use of ChatGPT and GPT-4. For in-


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stance, there are concerns about the generation of biased or harmful content,
privacy violations, and the potential for misuse of the technology. It is crucial
to address these concerns and ensure that ChatGPT and GPT-4 are developed
and used in a responsible and ethical manner. Furthermore, the results of this
study demonstrate that there is signiﬁcant potential for ChatGPT and GPT-4
26


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to be applied in a range of domains, including education, history, mathematics,
physics, and more. These models can facilitate tasks such as generating sum-
maries, answering questions, and providing personalized recommendations to
users. Overall, the insights presented in this review paper can serve as a useful
guide for researchers and practitioners looking to advance the ﬁeld of natural
language processing. Future research in this ﬁeld should focus on addressing


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ethical concerns, exploring new applications, and ensuring the responsible use
of ChatGPT and GPT-4. The potential of these models to revolutionize natural
language processing is enormous, and we look forward to seeing more develop-
ments in this ﬁeld.
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Summary of ChatGPT/GPT-4 Research
and Perspective Towards the Future of Large
Language Models
Yiheng Liu ∗1, Tianle Han ∗1, Siyuan Ma 1, Jiayue Zhang 1,
Yuanyuan Yang1, Jiaming Tian 1, Hao He 1, Antong Li 2, Mengshen
He1, Zhengliang Liu 3, Zihao Wu 3, Dajiang Zhu 4, Xiang Li 5, Ning
Qiang1, Dingang Shen 6,7,8, Tianming Liu 3, and Bao Ge †1
1School of Physics and Information Technology, Shaanxi Normal University, Xi’an
710119 China
2School of Life and Technology Biomedical-Engineering, Xi’an Jiaotong University,
Xi’an 710049, China
3School of Computing, The University of Georgia, Athens 30602, USA
4Department of Computer Science and Engineering, The University of Texas at
Arlington, Arlington 76019, USA
5Department of Radiology, Massachusetts General Hospital and Harvard Medical
School, Boston 02115, USA
6School of Biomedical Engineering, ShanghaiTech University, Shanghai 201210,
China
7Shanghai United Imaging Intelligence Co., Ltd., Shanghai 200230, China


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China
7Shanghai United Imaging Intelligence Co., Ltd., Shanghai 200230, China
8Shanghai Clinical Research and Trial Center, Shanghai 201210, China
Abstract
This paper presents a comprehensive survey of ChatGPT and GPT-4,
state-of-the-art large language models (LLM) from the GPT series, and
their prospective applications across diverse domains. Indeed, key innova-
tions such as large-scale pre-training that captures knowledge across the
entire world wide web, instruction ﬁne-tuning and Reinforcement Learn-
ing from Human Feedback (RLHF) have played signiﬁcant roles in en-
hancing LLMs’ adaptability and performance. We performed an in-depth
analysis of 194 relevant papers on arXiv, encompassing trend analysis,
word cloud representation, and distribution analysis across various appli-
cation domains. The ﬁndings reveal a signiﬁcant and increasing interest
in ChatGPT/GPT-4 research, predominantly centered on direct natural


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in ChatGPT/GPT-4 research, predominantly centered on direct natural
language processing applications, while also demonstrating considerable
potential in areas ranging from education and history to mathematics,
∗Co-ﬁrst author
†Corresponding author: bob ge@snnu.edu.cn
1
arXiv:2304.01852v1  [cs.CL]  4 Apr 2023


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medicine, and physics. This study endeavors to furnish insights into Chat-
GPT’s capabilities, potential implications, ethical concerns, and oﬀer di-
rection for future advancements in this ﬁeld.
1 Introduction
Recent advances in natural language processing (NLP) have led to the devel-
opment of powerful language models such as the GPT (Generative Pre-trained
Transformer) series [1, 2, 3, 4, 5], including large language models (LLM) such
as ChatGPT and GPT-4. These models are pre-trained on vast amounts of
text data and have demonstrated exceptional performance in a wide range of
NLP tasks, including language translation, text summarization, and question-
answering. In particular, the ChatGPT model has demonstrated its potential
in various ﬁelds, including education, healthcare, reasoning, text generation,
human-machine interaction, and scientiﬁc research.
A key milestone of LLM development is InstructGPT [5], a framework that


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A key milestone of LLM development is InstructGPT [5], a framework that
allows for instruction ﬁne-tuning of a pre-trained language model based on Re-
inforcement Learning from Human Feedback (RLHF) [6, 5]. This framework
enables an LLM to adapt to a wide range of NLP tasks, making it highly ver-
satile and ﬂexible by leveraging human feedback. RLHF enables the model to
align with human preferences and human values, which signiﬁcantly improves
from large language models that are solely trained text corpora through unsu-
pervised pre-training. ChatGPT is a successor to InstructGPT. Since its release
in December 2022, ChatGPT has been equipped with these advanced develop-
ments, leading to impressive performances in various downstream NLP tasks
such as reasoning and generalized text generation. These unprecedented NLP
capabilities spur applications in diverse domains such as education, healthcare,
human-machine interaction, medicine and scientiﬁc research. ChatGPT has re-


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human-machine interaction, medicine and scientiﬁc research. ChatGPT has re-
ceived widespread attention and interest, leading to an increasing number of
applications and research that harness its exceeding potential. The open release
of the multi-modal GPT-4 model further expands the horizon of large language
models and empowers exciting developments that involve diverse data beyond
text.
The purpose of this paper is to provide a comprehensive survey of the existing
research on ChatGPT and its potential applications in various ﬁelds. To achieve
this goal, we conducted a thorough analysis of papers related to ChatGPT in
the arXiv repository. As of April 1st, 2023, there are a total of 194 papers
mentioning ChatGPT on arXiv. In this study, we conducted a trend analysis of
these papers and generated a word cloud to visualize the commonly used terms.
Additionally, we also examined the distribution of the papers across various


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Additionally, we also examined the distribution of the papers across various
ﬁelds and presented the corresponding statistics. Figure 1 displays the daily
submission trend of papers related to ChatGPT, indicating a growing interest
in this ﬁeld. Figure 2 illustrates the word cloud analysis of all the papers. We
can observe that the current research is primarily focused on natural language
processing, but there is still signiﬁcant potential for research in other ﬁelds such
2


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Figure 2: Word cloud analysis of all the 194 papers.
as education and history. This is further supported by Figure 3, which displays
the distribution of submitted papers across various ﬁelds, highlighting the need
for more research and development in these areas.
This paper aims to shed light on the promising capabilities of ChatGPT
and provide insight into its potential impact in the future, including ethical
considerations. Through this survey, we hope to provide insights into how these
models can be improved and extended in the future. In section 2, we will
review the existing work related to ChatGPT, including its applications, ethical
considerations, and evaluation. In addition to discussing the current state of
research related to ChatGPT, we will also explore its limitations in section 3.
Furthermore, we will provide guidance on future directions for language model
development.
2 Related work of ChatGPT


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and/or conceptual subjects such as philosophy and religion. Additionally, users
can ask open-ended and analytical questions to understand the capabilities of
ChatGPT.
In the ﬁeld of mathematics, Frieder et al. [7] constructed the GHOSTS nat-
ural language dataset, which consists of graduate-level math test questions.
The authors tested ChatGPT’s math abilities on the GHOSTS dataset us-
ing a question-and-answer format and evaluated it according to ﬁne-grained
standards.In the Grad Text dataset, which covers simple set theory and logic
problems, ChatGPT performed the best. However, in the Olympiad-Problem-
Solving dataset, ChatGPT performed poorly, receiving only two 4-point scores
(out of a total of 5), with the majority of scores being 2 points. In the Holes-
in-Proofs dataset, ChatGPT received the lowest score of 1 point. In the MATH
dataset, ChatGPT only scored impressively in 26% of cases. These results sug-


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dataset, ChatGPT only scored impressively in 26% of cases. These results sug-
gest that ChatGPT’s math abilities are clearly lower than those of ordinary
math graduate students. Although ChatGPT can generally understand math
problems, it fails to provide the correct solutions. Pardos et al. [8] used the
Open Adaptive Tutoring system (OATutor) to investigate whether prompts gen-
erated by ChatGPT were helpful for learning algebra, with 77 participants from
Mechanical Turk taking part in the experiment. The experiment used ques-
tions from OpenStax’s Elementary and Intermediate Algebra textbooks. These
participants were randomly assigned to either a control group (with manual
prompts) or an experimental group (with ChatGPT prompts). For each ques-
tion in both courses, the authors obtained answers from ChatGPT through a
question-and-answer format and evaluated scores according to three criteria:
ChatGPT provided an answer, the answer was correct, and inappropriate lan-


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ChatGPT provided an answer, the answer was correct, and inappropriate lan-
guage was not used in the answer. The study found that 70% of prompts gener-
ated by ChatGPT passed manual quality checks, and both humans and Chat-
GPT produced positive learning gains. However, the scores of human prompts
ranged from 74.59% to 84.32%, signiﬁcantly higher than those of ChatGPT
prompts. Shakarian et al. [9] studied the performance of ChatGPT on math
word problems (MWPs), using the DRAW-1K dataset for experimentation. The
dataset consists of 1000 MWPs and their answers, along with algebraic equa-
tion templates for solving such problems. The authors used the idea of machine
learning introspection and built performance prediction models using random
forests and XGBoost, and evaluated them on the dataset using ﬁve-fold cross-
validation. ChatGPT’s accuracy increased from an initial 34% to a ﬁnal 69%,
while its recall increased from an initial 41% to a ﬁnal 83%. The authors also


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while its recall increased from an initial 41% to a ﬁnal 83%. The authors also
found that ChatGPT’s failure rate decreased from an initial 84% to a ﬁnal
20%, indicating that performance can vary greatly depending on speciﬁc job
requirements.
In the ﬁeld of physics, Lehnert et al. [10] explored the capabilities and limita-
tions of ChatGPT by studying how it handles obscure physics topics such as the
swamp land conjecture in string theory. The experimental dialogue began with
broader and more general questions in the ﬁeld of string theory before narrowing
down to speciﬁc swamp land conjectures and examining ChatGPT’s understand-
ing of them. The study found that ChatGPT could deﬁne and explain diﬀerent
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concepts in various styles, but was not eﬀective in truly connecting various con-
cepts. It would conﬁdently provide false information and fabricate statements
when necessary, indicating that ChatGPT cannot truly create new knowledge
or establish new connections. However, in terms of identifying analogies and
describing abstract concepts of visual representation, ChatGPT can cleverly
use language. Kortemeyer et al. [11] evaluated ChatGPT’s ability to answer
calculus-based physics questions through a question-and-answer test. The tests
included online homework, clicker questions, programming exercises, and exams
covering classical mechanics, thermodynamics, electricity and magnetism, and
modern physics. While ChatGPT was able to pass the course, it also demon-
strated many misconceptions and errors commonly held by beginners. West et
al. [12] used the Force Concept Inventory (FCI) to evaluate ChatGPT’s accu-
racy in answering physics concept problems related to kinematics and Newtonian


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racy in answering physics concept problems related to kinematics and Newtonian
mechanics in the ﬁrst semester of college physics. The FCI covers topics such
as kinematics, projectile motion, free fall, circular motion, and Newton’s laws.
The study included data from 415 students who took the FCI at the end of the
semester, with an average score of 56%, while ChatGPT scored approximately
between 50% to 65%. The authors demonstrated that ChatGPT’s performance
in physics learning can reach or even exceed the average level of a semester of
college physics.
In the medical ﬁeld
ChatGPT’s question-answering capabilities can also be applied in the med-
ical ﬁeld, such as for answering medical questions from patients or assisting
healthcare professionals in diagnosing diseases. Nov et al. [13] evaluated the fea-
sibility of using ChatGPT for patient-doctor communication. The experiment
extracted 10 representative patient-doctor interactions from EHR, placed the


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extracted 10 representative patient-doctor interactions from EHR, placed the
patient’s questions in ChatGPT, and asked ChatGPT to respond using roughly
the same number of words as the doctor’s response. Each patient’s question was
answered by either the doctor or ChatGPT, and the patient was informed that
5 were answered by the doctor and 5 were generated by ChatGPT, and was
asked to correctly identify the source of the response. The results of the exper-
iment showed that the probability of correctly identifying ChatGPT’s response
was 65.5%, while the probability of correctly identifying the doctor’s response
was 65.1%. In addition, the experiment found that the patient’s response to
the trustworthiness of ChatGPT’s function was weakly positive (average Likert
score: 3.4), and trust decreased as the complexity of health-related tasks in
the questions increased. ChatGPT’s responses to patient questions were only


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the questions increased. ChatGPT’s responses to patient questions were only
slightly diﬀerent from those of doctors, but people seem to trust ChatGPT to
answer low-risk health questions, while for complex medical questions, people
still tend to trust the doctor’s responses and advice.
Tu et al. [14] explored the causal discovery ability of ChatGPT in the diag-
nosis of neuropathic pain. Causal relationship discovery aims to reveal potential
unknown causal relationships based purely on observed data [15]. The experi-
mental results found that ChatGPT has some limitations in understanding new
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knowledge and concepts beyond the existing textual training data corpus, that
is, it only understands language commonly used to describe situations and not
underlying knowledge. In addition, its performance consistency and stability are
not high, as the experiment observed that it would provide diﬀerent answers for
the same question under multiple inquiries. However, despite the many limita-
tions of ChatGPT, we believe that it has a great opportunity to improve causal
relationship research.
In the other ﬁeld
Guo et al. [16] attempted to apply ChatGPT in the ﬁeld of communication,
speciﬁcally using ChatGPT for ordered importance semantic communication,
where ChatGPT plays the role of an intelligent consulting assistant that can
replace humans in identifying the semantic importance of words in messages and
can be directly embedded into the current communication system. For a message
to be transmitted, the sender ﬁrst utilizes ChatGPT to output the semantic


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to be transmitted, the sender ﬁrst utilizes ChatGPT to output the semantic
importance order of each word. Then, the transmitter executes an unequal error
protection transmission strategy based on the importance order to make the
transmission of important words in the message more reliable. The experimental
results show that the error rate and semantic loss of important words measured
in the communication system embedded with ChatGPT are much lower than
those of existing communication schemes, indicating that ChatGPT can protect
important words well and make semantic communication more reliable.
Wang et al. [17] studied the eﬀectiveness of ChatGPT in generating high-
quality Boolean queries for systematic literature search. They designed a wide
range of prompts and investigated these tasks on more than 100 systematic
review topics. In the end, queries generated by ChatGPT achieved higher accu-
racy compared to the currently most advanced query generation methods but


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racy compared to the currently most advanced query generation methods but
at the cost of reduced recall. For time-limited rapid reviews, it is often accept-
able to trade oﬀ higher precision for lower recall. Additionally, ChatGPT can
generate high search accuracy Boolean queries by guiding the prompts. How-
ever, it should be noted that when two queries use the same prompts, ChatGPT
generates diﬀerent queries, indicating its limitations in consistency and stabil-
ity. Overall, this study demonstrated the potential of ChatGPT in generating
eﬀective Boolean queries for systematic literature searches.
2.1.2 Text Classiﬁcation
The purpose of text classiﬁcation is to assign text data to predeﬁned categories.
This task is critical for many applications, including sentiment analysis, spam
detection, and topic modeling. While traditional machine learning algorithms
have been widely used for text classiﬁcation, recent advances in natural lan-


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have been widely used for text classiﬁcation, recent advances in natural lan-
guage processing have led to the development of more advanced techniques.
ChatGPT has shown immense potential in this ﬁeld. Its ability to accurately
classify text, ﬂexibility in handling various classiﬁcation tasks, and potential for
customization make it a valuable tool for text classiﬁcation, as evidenced by
several studies in the literature.
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Kuzman et al. [18] employed ChatGPT for automatic genre recognition, with
the goal of simplifying the text classiﬁcation task by utilizing ChatGPT’s zero-
shot classiﬁcation capability. They compared ChatGPT’s genre recognition per-
formance, using two prompt languages (EN and SL), with the X-GENRE clas-
siﬁer based on the multilingual model XLM-RoBERTa on the English dataset
EN-GINCO and the Slovenian dataset GINCO. The results showed that when
EN was used as the prompt language, ChatGPT achieved Micro F1, Macro F1,
and Accuracy scores of 0.74, 0.66, and 0.72. However, on the GINCO dataset,
ChatGPT’s genre recognition performance with both EN and SL prompt lan-
guages was lower than that of the X-GENRE classiﬁer to varying degrees.
Amin et al. [19] evaluated the text classiﬁcation ability of ChatGPT in
aﬀective computing by using it to perform personality prediction, sentiment
analysis, and suicide ideation detection tasks. They prompted ChatGPT with


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analysis, and suicide ideation detection tasks. They prompted ChatGPT with
corresponding prompts on three datasets: First Impressions, Sentiment140, and
Suicide and Depression, and compared its classiﬁcation performance with three
baseline models: RoBERTa-base, Word2Vec, and BoW. The results showed
that ChatGPT’s accuracy and UAR for the ﬁve personality classiﬁcations on
the First Impressions dataset were lower than the baseline methods to varying
degrees. On the Sentiment140 dataset, ChatGPT’s accuracy and UAR were 85.5
and 85.5, respectively, which were better than the three baseline methods. On
the Suicide and Depression dataset, ChatGPT’s accuracy and UAR were 92.7
and 91.2, respectively, which were lower than RoBERTa, the best-performing
baseline method.
Zhang et al. [20] employed ChatGPT for stance detection, which includes
support and opposition. They used ChatGPT to classify the political stance
of tweets in the SemEval-2016 and P-Stance datasets. SemEval-2016 contains


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of tweets in the SemEval-2016 and P-Stance datasets. SemEval-2016 contains
4,870 English tweets, and they selected tweets with the most commonly occur-
ring FM, LA, and HC political labels for stance classiﬁcation. The P-Stance
dataset has 21,574 English tweets, and they classiﬁed the stance of tweets to-
wards Trump, Biden, and Bernie. The ﬁnal results showed that on the SemEval-
2016 dataset, ChatGPT achieved F1-m scores of 68.4, 58.2, and 79.5 for the FM,
LA, and HC political labels, and F1-avg scores of 72.6, 59.3, and 78.0, respec-
tively. On the P-Stance dataset, ChatGPT achieved F1-m scores of 82.8, 82.3,
and 79.4 for the Trump, Biden, and Bernie political ﬁgures, and F1-avg scores
of 83.2, 82.0, and 79.4, respectively.
Huang et al. [21] used ChatGPT to detect implicit hate speech in tweets.
They selected 12.5% (795 tweets) of the LatentHatred dataset containing im-
plicit hate speech and asked ChatGPT to classify them into three categories:


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plicit hate speech and asked ChatGPT to classify them into three categories:
implicit hate speech, non-hate speech, and uncertain. The results showed that
ChatGPT correctly recognized 636 (80%) of the tweets. The number of tweets
classiﬁed as non-hate speech and uncertain were 146 (18.4%) and 13 (1.6%),
respectively. The results of the reclassiﬁcation of tweets in the non-hate speech
and uncertain categories by Amazon Mechanical Turk (Mturk) workers were
consistent with ChatGPT’s classiﬁcation.
Overall, ChatGPT has tremendous potential in text classiﬁcation tasks, as it
can eﬀectively address problems such as genre identiﬁcation, sentiment analysis,
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stance detection, and more. However, there are still challenges that ChatGPT
faces in the ﬁeld of text classiﬁcation. Firstly, it struggles to perform well in
classiﬁcation tasks with rare or out-of-vocabulary words since it heavily relies
on the distribution of training data. Additionally, the signiﬁcant computational
resources required for training and utilizing ChatGPT can limit its use in some
applications.
2.1.3 Text generation
We live in an era of information explosion, and text is an eﬃcient way of trans-
mitting information. The diversity of information has led to a diversity of text
categories. When researchers use ChatGPT’s text generation capabilities for
research, they inevitably choose to generate diﬀerent types of text. In the pro-
cess of reading papers, we found that the word count of the text generated by
researchers increased from small to large, so we wanted to summarize existing
research based on the size of the text word count. We divided the generated


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research based on the size of the text word count. We divided the generated
text into three levels: phrases, sentences, and paragraphs.
The following article uses ChatGPT to generate phrases. Zhang et al. [22]
proves that the semantic HAR model with semantic augmentation added dur-
ing training performs better in motion recognition than other models. Semantic
augmentation requires shared tokens, which is lacking in some datasets. There-
fore, authors leverage ChatGPT for an automated label generation approach
for datasets originally without shared tokens. Fu et al. [23] describes a new
workﬂow for converting natural language commands into Bash commands. The
author uses ChatGPT to generate a candidate list of Bash commands based
on user input, and then uses a combination of heuristic and machine learning
techniques to rank and select the most likely candidates. This workﬂow was
evaluated on a real command dataset and achieved high accuracy compared


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evaluated on a real command dataset and achieved high accuracy compared
to other state-of-the-art methods. Chen et al. [24] used the Bart model and
ChatGPT for the task of summarizing humorous titles and compared the perfor-
mance of the two models. It was found that the Bart model performed better on
large datasets, but ChatGPT was competitive with our best ﬁne-tuned model
in a small range (48), albeit slightly weaker.
The following article uses ChatGPT to generate sentences.Chen et al. [25]
constructed a dialogue dataset (HPD) with scenes, timelines, character at-
tributes, and character relationships in order to use ChatGPT as a conver-
sational agent to generate dialogue. However, ChatGPT’s performance on the
test set was poor, and there is room for improvement.In study [26], chatGPT
demonstrated its ability to simplify complex text by providing three ﬁctional
radiology reports to chatGPT for simpliﬁcation. Most radiologists found the


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radiology reports to chatGPT for simpliﬁcation. Most radiologists found the
simpliﬁed reports to be accurate and complete, with no potential harm to pa-
tients. However, some errors, omissions of critical medical information and text
passages were identiﬁed, which could potentially lead to harmful conclusions if
not understood by the physicians. Xia et al. [27] proposes a new program re-
pair paradigm called Session-based Automatic Program Repair (APR). In APR,
the previously generated patches are iteratively built upon by combining them
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with validation feedback to construct the model’s input. The eﬀectiveness of
the approach is veriﬁed using the QuixBugs dataset. The experiment shows
that ChatGPT ﬁne-tuned with reinforcement learning from human feedback
(RLHF) outperforms Codex trained unsupervisedly in both repair datasets. In
reference to study [28], ChatGPT was compared to three commercial transla-
tion products: Google Translate2, DeepL Translate3, and Tencent TranSmart4.
The evaluation was conducted on the Flores101 test set, using the WMT19
biomedical translation task to test translation robustness, with BLEU score as
the main metric. The study found that ChatGPT is competitive with commer-
cial translation products on high-resource European languages but falls behind
on low-resource or distant languages. The authors explored an interesting strat-
egy called pivot prompts, which signiﬁcantly improved translation performance.
While ChatGPT did not perform as well as commercial systems on biomedical


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While ChatGPT did not perform as well as commercial systems on biomedical
abstracts or Reddit comments, it may be a good speech translator. Prieto et
al. [29] evaluated the use of ChatGPT in developing an automated construction
schedule based on natural language prompts. The experiment required building
new partitions in an existing space and providing details on the rooms to be
partitioned. The results showed that ChatGPT was able to generate a coher-
ent schedule that followed a logical approach to meet the requirements of the
given scope. However, there were still several major ﬂaws that would limit the
use of this tool in real-world projects.Michail et al. [30] proposed a method
to improve the prediction accuracy of the HeFit ﬁne-tuned XLM T model on
tweet intimacy by generating a dataset of tweets with intimacy rating tags using
ChatGPT. The speciﬁc operation is to input tweets with intimacy rating tags
into ChatGPT and then output similar tweets.


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into ChatGPT and then output similar tweets.
The following article uses ChatGPT to generate paragraphs. Wang et al.
[31] compared the abstract summarization performance of ChatGPT and other
models on various cross-lingual text datasets and found that ChatGPT may
perform worse in metrics such as R 1, R 2, R L, and B S. Yang et al. [32]
summarized the performance of ChatGPT in question answering-based text
summarization and found that, compared to ﬁne-tuned models, ChatGPT’s
performance is slightly worse in all performance metrics. However, the article
suggests that if the dataset is golden annotation, ChatGPT’s performance may
surpass ﬁne-tuned models in these metrics. Belouadi et al. [33] compared the
ability of ByGPT5 and ChatGPT trained on a range of labeled and unlabeled
datasets of English and German poetry to generate constrained style poetry, and
evaluated them using three metrics: Rhyme, ScoreAlliteration, and ScoreMe-


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evaluated them using three metrics: Rhyme, ScoreAlliteration, and ScoreMe-
ter Score. The conclusion is that ByGPT5 performs better than ChatGPT.
Blanco-Gonzalez et al. [34] evaluated chatGPT’s ability to write commentary
articles, and in fact, this article itself was written by chatGPT. The human au-
thor rewrote the manuscript based on chatGPT’s draft. Experts found that it
can quickly generate and optimize text, as well as help users complete multiple
tasks. However, in terms of generating new content, it is not ideal. Ultimately,
it can be said that without strong human intervention, chatGPT is not a use-
ful tool for writing reliable scientiﬁc texts. It lacks the knowledge and expertise
required to accurately and fully convey complex scientiﬁc concepts and informa-
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tion. Khalil et al. [35] on the originality of content generated by ChatGPT. To
evaluate the originality of 50 papers on various topics generated by ChatGPT,
two popular plagiarism detection tools, Turnitin and iThenticate, were used.
The results showed that ChatGPT has great potential in generating complex
text output that is not easily captured by plagiarism detection software. The
existing plagiarism detection software should update their plagiarism detection
engines. Basic et al. [36] conducted a comparison of the writing performance
of students using or not using ChatGPT-3 as a writing aid. The experiment
consisted of two groups of 9 participants each. The control group wrote articles
using traditional methods, while the experimental group used ChatGPT as an
aid. Two teachers evaluated the papers. The study showed that the assistance of
ChatGPT did not necessarily improve the quality of the students’ essays.Noever


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ChatGPT did not necessarily improve the quality of the students’ essays.Noever
et al. [37] discusses the potential of using artiﬁcial intelligence (AI), particularly
language models like GPT (including GPT-3), to create more convincing chat-
bots that can deceive humans into thinking they are interacting with another
person. The article describes a series of experiments in which they used GPT-3
to generate chatbot responses that mimic human-like conversations and were
tested on human participants. The results show that some participants were
unable to distinguish between the chatbot and a real human, highlighting the
potential for these AI chatbots to be used for deceptive purposes.
2.1.4 Code Generation
Code generation refers to the process of automatically generating computer code
from high-level descriptions or speciﬁcations. ChatGPT’s advanced natural lan-
guage processing capabilities make it capable of performing code generation


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guage processing capabilities make it capable of performing code generation
tasks. By analyzing the requirements for code generation, ChatGPT can pro-
duce code snippets that accurately execute the intended functionality. This not
only saves time and eﬀort in writing code from scratch but also reduces the risk
of errors that may occur during manual coding. In addition, ChatGPT’s ability
to learn and adapt to new programming languages and frameworks enables it
to complete more complex programming tasks. For example:
Megahed et al. [38] discussed the potential of using ChatGPT for tasks
such as code explanation, suggesting alternative methods for problem-solving
with code, and translating code between programming languages. The solutions
provided by ChatGPT were found to be viable. In another study, Treude et
al. [39] introduced a ChatGPT-based prototype called GPTCOMCARE, which
helps programmers generate multiple solutions for a programming problem and


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helps programmers generate multiple solutions for a programming problem and
highlight the diﬀerences between each solution using colors.Sobania et al. [40]
utilized ChatGPT for code bug ﬁxing, and further improved the success rate of
bug ﬁxing by inputting more information through its dialogue system. Speciﬁ-
cally, the QuixBugs standard bug ﬁxing benchmark contained 40 code bugs that
needed to be ﬁxed. With limited information, ChatGPT ﬁxed 19 bugs, which
was slightly lower than the 21 bugs ﬁxed by the Codex model, but signiﬁcantly
higher than the 7 ﬁxed by the Standard APR model. When given more prompts
and information, ChatGPT was able to ﬁx 31 bugs, demonstrating its potential
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for code bug ﬁxing tasks.Xia et al. [27] proposed a conversational approach
for Automated Program Repair (APR), which alternates between generating
patches and validating them against feedback from test cases until the correct
patch is generated. Selecting 30 bugs from the QuixBugs standard bug ﬁxing
benchmark, which are suitable for test case feedback, and demonstrating them
with Java and Python, the QuixBugs-Python and QuixBugs-Java datasets were
obtained. The conversational APR using ChatGPT outperformed the conversa-
tional APR using Codex and the conversational APR using CODEGEN (with
model parameters of 350M, 2B, 6B, and 16B) on both datasets. Furthermore,
ChatGPT’s conversational APR generated and validated patches with signiﬁ-
cantly fewer feedback loops than the other models.
ChatGPT can not only be used to achieve some simple code generation tasks
but also can be used to accomplish some complex programming tasks. Noever


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but also can be used to accomplish some complex programming tasks. Noever
et al. [41] tested ChatGPT’s code generation capabilities on four datasets -
Iris, Titanic, Boston Housing, and Faker. When prompted to mimic a Python
interpreter in the form of a Jupyter notebook, the model was able to generate
independent code based on the prompt and respond with the expected out-
put. For example, when given the prompt ”data.cor()” for the Iris dataset,
ChatGPT generated correct Python output. The test results indicate that
ChatGPT can access structured datasets and perform basic software opera-
tions required by databases, such as create, read, update, and delete (CRUD).
This suggests that cutting-edge language models like ChatGPT have the nec-
essary scale to tackle complex problems. McKee et al. [42] utilized ChatGPT
as an experimental platform to investigate cybersecurity issues. They modeled
ﬁve diﬀerent modes of computer virus properties, including self-replication, self-


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ﬁve diﬀerent modes of computer virus properties, including self-replication, self-
modiﬁcation, execution, evasion, and application, using ChatGPT. These ﬁve
modes encompassed thirteen encoding tasks from credential access to defense
evasion within the MITRE ATT&CK framework. The results showed that the
quality of ChatGPT’s generated code was generally above average, except for the
self-replication mode, where it performed poorly.They [43] also employed Chat-
GPT as a network honeypot to defend against attackers. By having ChatGPT
mimic Linux, Mac, and Windows terminal commands and providing interfaces
for TeamViewer, nmap, and ping, a dynamic environment can be created to
adapt to attackers’ operations, and logs can be used to gain insight into their
attack methods, tactics, and procedures. The authors demonstrated ten hon-
eypot tasks to illustrate that ChatGPT’s interface not only provides suﬃcient
API memory to execute previous commands without defaulting to repetitive


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API memory to execute previous commands without defaulting to repetitive
introductory tasks but also oﬀers a responsive welcome program that maintains
attackers’ interest in multiple queries.
In the ﬁeld of code generation, there are still several challenges with Chat-
GPT. Firstly, its application scope is limited as its training data is biased to-
wards programming languages such as Python, C++, and Java, making it po-
tentially unsuitable for some programming languages or coding styles. Secondly,
manual optimization is necessary for code formatting, as the generated code may
not be performance-optimized or follow best coding practices, requiring manual
editing and optimization. Lastly, the quality of the generated code cannot be
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guaranteed, as it heavily relies on the quality of the natural language input,
which may contain errors, ambiguities, or inconsistencies, ultimately aﬀecting
the accuracy and reliability of the generated code.
2.1.5 Inference
Inference refers to the process of drawing new conclusions or information through
logical deduction from known facts or information. It is typically based on a se-
ries of premises or assumptions, and involves applying logical rules or reasoning
methods to arrive at a conclusion. Inference is an important ability in human
thinking, and is often used to solve problems, make decisions, analyze and eval-
uate information, etc. Inference also plays a key role in ﬁelds such as science,
philosophy, law, etc. There are two types of inference: inductive reasoning,
which involves deriving general rules or conclusions from known facts or expe-
riences, and deductive reasoning, which involves deriving speciﬁc conclusions


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riences, and deductive reasoning, which involves deriving speciﬁc conclusions
from known premises or assumptions. Whether inductive or deductive, the pro-
cess of inference requires following strict logical rules to ensure the correctness
and reliability of the inference.
Some papers attempt to use ChatGPT’s ability in inductive reasoning to
capture the meaning in text and use deﬁned metrics to score the text. Michail
et al. [30] uses ChatGPT to infer intimacy expressed in tweets. They ﬁrst input
50 tweets with intimacy markers to ChatGPT, then use inductive reasoning to
infer the standards for generating tweets with diﬀerent levels of intimacy, and
ﬁnally generate ten tweets with intimacy values ranging from 0 to 5. Susnjak
et al. [44] collected a large amount of textual data from patient-doctor dis-
cussion forums, patient testimonials, social media platforms, medical journals,
and other scientiﬁc research publications. Using the BERT model, the author


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and other scientiﬁc research publications. Using the BERT model, the author
inferred emotion values from 0 to 1. The author visualized the process of how
the presence of bias in the discourse surrounding chronic manifestations of the
disease using the SHAP tool. The author also envisioned ChatGPT as a replace-
ment for the BERT model for scoring the emotional value of text. Huang et al.
[21] chose 12.5% of individuals in the potential hate dataset as study materials,
induced ChatGPT to make classiﬁcations based on a prompt, and ChatGPT
produced three classiﬁcations: unclear, yes, and no. The author assigned a
value of 1 to yes, -1 to no, and 0 to unclear, and had ChatGPT score and clas-
sify them. ChatGPT was able to correctly classify 80% of implicit hate tweets
in the author’s experimental setup, demonstrating ChatGPT’s great potential
as a data labeling tool using simple prompts.
Some papers have evaluated ChatGPT’s reasoning performance, mainly in


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Some papers have evaluated ChatGPT’s reasoning performance, mainly in
decision-making and spatial reasoning, and identifying ambiguity. Tang et al.
[45] used the independence axiom and the transitivity axiom, as well as other
non-VNM related decision-making abilities, by presenting bets conditioned on
random events, bets with asymmetric outcomes, decisions encapsulating Sav-
age’s Sure Thing principle, and other complex bet structures like nested bets, to
design experiments where each experiment input a short prompt to ChatGPT
and evaluated the results. The conclusion is that ChatGPT exhibits uncer-
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tainty in the decision-making process: in some cases, large language models
can arrive at the correct answer through incorrect reasoning; and it may make
suboptimal decisions for simple reasoning problems. Ortega-Martn et al. [46]
had ChatGPT detect three diﬀerent levels of language ambiguity and evaluated
its performance. The conclusion is that In semantics, ChatGPT performed per-
fectly in the detection of ambiguities. Apart from that, it has some bright sports
(co-reference resolution) and some weaknesses (puts gender bias over grammar
in some non-ambiguous situations). In the generation task ChatGPT did well,
but also revealed some of its worse issues: the lack of systematicity. Lastly, it
should also be pointed that in most of the cases ChatGPT brilliantly alludes to
lack of context as the key factor in disambiguation.
2.1.6 Data or information extraction, transformation, enhancement,
processing
Data Visualization


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2.1.6 Data or information extraction, transformation, enhancement,
processing
Data Visualization
Natural language interfaces have contributed to generating visualizations
directly from natural language, but visualization problems remain challenging
due to the ambiguity of natural language.ChatGPT provides a new avenue for
the ﬁeld by converting natural language into visualized code.
In terms of data visualization, Noever et al. [41] tested ChatGPT’s ba-
sic arithmetic skills by asking questions.On the iris dataset, Titanic survival
dataset, Boston housing data, and randomly generated insurance claims dataset,
the statistical analysis of data and visualization problems were converted to
programming problems using Jupyter to verify ChatGPT’s ability to generate
python code to draw suitable graphs and analyze the data. The results show
that ChatGPT can access structured and organized datasets to perform the
four basic software operations required for databases: create, read, update, and


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four basic software operations required for databases: create, read, update, and
delete, and generate suitable python code to plot graphs for descriptive statis-
tics, variable correlation analysis, describing trends, and other data analysis
operations.Maddigan et al. [47] proposed an end-to-end solution for visualizing
data in natural language using LLM, which uses an open-source python frame-
work designed to generate appropriate hints for selected datasets to make LLM
more eﬀective in understanding natural language, and uses internal reasoning
capabilities to select the appropriate visualization type to generate the code for
visualization. In this paper,the reseachers compare the visualization results of
GPT-3, Codex and ChatGPT in the case of nvBench SQLite database [48] and
the visualization results of energy production dataset in the study of ADVISor
with NL4DV [49, 50].In addition to, they explore the ability to reason and hy-


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with NL4DV [49, 50].In addition to, they explore the ability to reason and hy-
pothesize of the LLM on movie dataset [48] when the hints are insuﬃcient or
wrong .Experimental results show that LLM can eﬀectively support the end-to-
end generation of visualization results from natural language when supported
by hints, providing an eﬃcient, reliable and accurate solution to the natural
language visualization problem.
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Information Extraction
The goal of information extraction is to extract speciﬁc information from
natural language text for structured representation, including three important
subtasks such as entity relationship extraction, named entity recognition, and
event extraction, which have wide applications in business, medical, and other
ﬁelds.
In information extraction, Wei et al. [51] proposed ChatIE, a ChatGPT-
based multi-round question-and-answer framework for information extraction.
The framework decomposes a complex information extraction (IE) task into
several parts, then combines the results of each round into a ﬁnal structured
result. The entity association triple extraction, named entity recognition, and
event extraction tasks were performed on six datasets NYT11-HRL, DuIE2.0
, conllpp, MSR , DuEE1.0 [52, 53, 54, 55, 56], and ACE05 in both languages,
comparing three metrics of precision, recall, and F1 score.These results sug-


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comparing three metrics of precision, recall, and F1 score.These results sug-
gest that on six widely used IE datasets, ChatIE improves performance by an
average of 18.98% compared to the original ChatGPT without ChatIE, and out-
performs the supervised models FCM and MultiR [57, 58] on the NYT11-HRL
dataset.While the original ChatGPT cannot solve complex IE problems with
original task instructions, and with this framework, successfully IE tasks were
implemented on six datasets.Gao et al. [59] explored the feasibility and chal-
lenges of ChatGPT for event extraction on the ACE2005 corpus, evaluating the
performance of ChatGPT in long-tail and complex scenarios (texts containing
multiple events) and comparing it with two task-speciﬁc models, Text2Event
and EEQA [60, 61].Then,they explored the impact of diﬀerent cues on perfor-
mance of ChatGPT. The results show that the average performance of Chat-
GPT in long-tail and complex scenarios is only 51.04% of that of task-speciﬁc


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GPT in long-tail and complex scenarios is only 51.04% of that of task-speciﬁc
models such as EEQA. Continuous reﬁnement of cues does not lead to consis-
tent performance improvements, and ChatGPT is highly sensitive to diﬀerent
cue styles.Tang et al. [62] proposed a new training paradigm that incorporates
appropriate cues to guide ChatGPT to generate a variety of examples with dif-
ferent sentence structures and language patterns and eliminate the resulting
low-quality or duplicate samples for downstream tasks. Although compared to
a soft model for a speciﬁc healthcare tasks, ChatGPT underperforms in Named
Entity Recognition (NER) and Relationship Extraction (RE) tasks , in the Gene
Association Database (GAD) Release; EU-ADR corpus for the RE task , the
innovative training framework was able to train local models, with F1 scores im-
proving from 23.37% to 63.99% for the named entity recognition task and from


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proving from 23.37% to 63.99% for the named entity recognition task and from
75%, while alleviating privacy concerns and time-consuming data collection and
annotation problems.He et al. [63] proposed a contextual learning framework
ICL- D3IE. this framework introduces formatted presentation, continuously it-
erates to update and improve the presentation, and then combines ChatGPT
for text information extraction. In the paper, ICL-D3IE is compared with ex-
isting pre-trained models such as LiLT,BROS (in-distribution (ID) setting and
out-of-distribution (OOD) setting) on datasets (FUNSD, CORD, and SROIE
[64, 65, 66]).These results show that the ICL-D3IE method in all datasets and
settings except for the ID setting on CORD are superior to other methods,
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with ICL-D3IE (GPT-3) F1 scores reaching 90.32% on FUNSD and97.88% on
SROIE; in the out-of-distribution (OOD) setting, ICL-D3IE performs much
better than previous pre-trained methods on all datasets.Polak et al. [67] pro-
posed ChatExtract method - consisting of a set of engineering prompts applied
to a conversational LLM - for automatic data extraction. In the experiment,
they extracted a large number of sentences from hundreds of papers and ran-
domly selected 100 sentences containing data and 100 sentences without data
as test data. The results show that the accuracy and recall of LLM exceeded
90% and may be comparable to human accuracy in many cases; in addition to
this, the experiments were conducted under the condition of removing follow-up
prompts and not keeping the conversation compared to previous experiments,
respectively. The accuracy of deleting follow-up questions dropped to 80.2%
and the recall rate dropped to 88.0%. Removing the conversational aspect and


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and the recall rate dropped to 88.0%. Removing the conversational aspect and
related information retention recall and accuracy dropped to 90.0% and 56.6%,
respectively, demonstrating the eﬀect of information retention combined with
purposeful redundancy on LLM information extraction performance.
Quality Assessment
For translation quality, text generation quality, manual assessment is usually
eﬀective but suﬀers from subjectivity and time-consuming, etc. It was found
through exploration that ChatGPT has also achieved signiﬁcant performance
in automatic quality assessment.
In terms of quality assessment,Kocmi et al. [68] proposed a GPT-based
translation quality assessment metric, GEMBA, which evaluates the transla-
tion of each fragment individually and then averages all the obtained scores to
obtain a ﬁnal system-level score. In the MQM2022 test set (English-German,
English-Russian, and Chinese-English) [69], a scoring task was performed with


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English-Russian, and Chinese-English) [69], a scoring task was performed with
a classiﬁcation task to compare the accuracy [70] and kendall tau scores [71] of
seven GPT models under four cue templates.The results showed that GEMBA
had the highest system-level accuracy of 88.0% compared to more than 10 au-
tomatic metrics such as BLEU, and among the seven GPT models, ChatGPT
accuracy is above 80%, in addition to, the best performance can be obtained in
the least constrained template, demonstrating the potential of LLM for trans-
lation quality assessment tasks, but the evaluation is only applicable at the
system level and needs further improvement.Wang et al. [72] used ChatGPT
as a natural language generation (NLG) evaluator to study the correlation with
human judgment. On three datasets covering diﬀerent NLG tasks, task- and
aspect-speciﬁc cues were designed to guide ChatGPT for NLG evaluation in
CNN/DM [73], OpenMEVA-ROC, and BAGEL for summary, story generation,


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CNN/DM [73], OpenMEVA-ROC, and BAGEL for summary, story generation,
and data-to-text scoring, respectively. Then,they compute Spearman coeﬃ-
cients [74],Pearson correlation coeﬃcients [75]. Kendall’s Tau score [76] to as-
sess the correlation with human evaluations.The results show that ChatGPT is
highly correlated with human judgments in all aspects, with correlation coeﬃ-
cients of 0.4 or more in all categories, showing its potential as an NLG indicator.
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Data Augmentation
In natural language processing, text data augmentation is an eﬀective mea-
sure to alleviate the problem of low data quantity and low quality training data,
and ChatGPT has shown great potential in this regard.
In terms of data augmentation, Dai et al. [77] proposed a ChatGPT-based
text data augmentation method that reformulates each sentence in the train-
ing sample into multiple conceptually similar but semantically diﬀerent samples
for classiﬁcation tasks downstream of the Bert model.On text transcriptions
and PubMed 20k datasets containing more than 8 hours of audio data of com-
mon medical symptom descriptions,experiments were conducted to compare co-
sine similarity and TransRate metrics with multiple data enhancement methods
[9].This paper shows that compared with existing data enhancement methods,
the proposed ChatAug method shows a double-digit improvement in sentence
classiﬁcation accuracy and generates more diverse augmented samples while


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classiﬁcation accuracy and generates more diverse augmented samples while
maintaining its accuracy, but the original model is not ﬁne-tuned in the pa-
per and suﬀers from a lack of domain knowledge, which may produce incorrect
augmented data.
Multimodal fusion
ChatGPT can currently only process natural language directly, but with a
cross-modal encoder, it can combine natural language with cross-modal pro-
cessing to provide solutions for intelligent transportation, healthcare, and other
ﬁelds.
In terms of multimodal data processing, Wu et al. [78] constructed a frame-
work that Visual ChatGPT integrates with diﬀerent Visual Foundation Models
(VFMs) and then combines a series of hints to input visual information to Chat-
GPT to solve visual problems.The paper shows examples of visual tasks such
as removing or replacing certain objects from images, interconversion between
images and text, demonstrating the Visual ChatGPT has great potential and


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images and text, demonstrating the Visual ChatGPT has great potential and
capability for diﬀerent tasks.But there are issues during the task that require
a large number of hints to convert VFMs to language, invoke multiple VFMs
to solve complex problems leading to limited real-time capability, and security
and privacy issues. Zheng et al. [79] showed a text mining example of LLM
for extracting self-driving car crash data from California crash news, analyz-
ing a failure report example, and generating a crash report example based on
keywords; introduced a use case concept of a smartphone-based framework for
automatic LLM failure report generation, which absorbs multiple data sources
captured by cell phone sensors and then transfers the data to a language space
for text mining, inference and generation, and further outputs the key informa-
tion needed to form a comprehensive fault report, demonstrating the potential
of LLM for a variety of transportation tasks.


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from devices such as sensors, and data privacy and security.
Cueing Techniques
Cue engineering provides important support for eﬀective dialogue with large
language models.White et al. [80] proposed a framework for cueing models
applicable to diﬀerent domains. This framework structures cues to interact
with LLMs by providing speciﬁc rules and guidelines. Also, this paper presents
a catalog of cueing patterns that have been applied to LLM interactions, as well
as speciﬁc examples with and without cues. The advantages of the combinability
of prompting patterns are demonstrated, allowing users to interact with LLM
more eﬀectively, but patterns for reusable solutions and new ways to use LLM
need to be continuously explored.
2.1.7 Human-ChatGPT Collaboration
Collaboration between humans and machines is a process where humans and
machines work together to achieve a common goal. In such collaboration, hu-
mans provide domain expertise, creativity, and decision-making abilities, while


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mans provide domain expertise, creativity, and decision-making abilities, while
machines provide automation, scalability, and computing power. ChatGPT is
an advanced natural language processing model that can understand and gen-
erate human-like language, thereby reducing communication costs. Its ability
to process and generate natural language makes it an ideal partner for human
collaboration. ChatGPT can oﬀer relevant suggestions, complete tasks based
on human input, and enhance human productivity and creativity. It can learn
from human feedback and adapt to new tasks and domains, further improv-
ing its performance in human-machine collaboration. ChatGPT’s capability to
comprehend natural language and produce appropriate responses makes it a
valuable tool for various collaboration applications, as demonstrated by several
studies in the literature we have gathered.
Ahmad et al. [81] proposed a method for human-machine collaboration us-


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Ahmad et al. [81] proposed a method for human-machine collaboration us-
ing ChatGPT to create software architecture. This method transforms software
stories (created by software architects based on application scenarios) into feasi-
ble software architecture diagrams through continuous interaction between the
software architect and ChatGPT. During the evaluation stage, ChatGPT uses
the Software Architecture Analysis Method (SAAM) to evaluate each compo-
nent in the software architecture and generate evaluation reports. This method
eﬃciently utilizes the knowledge and supervision of the architect with the ca-
pabilities of ChatGPT to collaboratively build software-intensive systems and
services. Lanzi et al. [82] proposed a collaborative design framework that com-
bines interactive evolution and ChatGPT to simulate typical human design pro-
cesses. Humans collaborate with large language models (such as ChatGPT) to


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cesses. Humans collaborate with large language models (such as ChatGPT) to
recombine and transform ideas, and use genetic algorithms to iterate through
complex creative tasks. The results of three game design tasks showed that the
framework received positive feedback from game designers. The framework has
good reusability and can be applied to any design task that can be described in
free text form.
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In the future, ChatGPT’s ability to understand nonverbal cues such as tone
of voice and body language can be enhanced, enabling it to better understand
human thoughts and interact with people more eﬀectively.
2.1.8 ChatGPT Integration
Integration refers to combining diﬀerent systems or software components to
achieve a common goal. ChatGPT can be integrated as a part of a whole or
act as an integration tool to enable seamless communication between diﬀerent
systems. Its natural language processing ability makes it easier for non-technical
users to interact with systems, reducing the need for specialized knowledge or
training. Some studies in the literature we collected have already demonstrated
this.
Treude et al. [39] integrated ChatGPT into the prototype of ”GPTCOM-
CARE” to address programming query problems. This integration allowed for
the generation of multiple source code solutions for the same query, which in-


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the generation of multiple source code solutions for the same query, which in-
creased the eﬃciency of software development. The results of their study demon-
strated the eﬀectiveness of using ChatGPT to improve the quality and diversity
of code solutions, ultimately reducing the amount of time and eﬀort required for
software development.Wang et al. [83] proposed the chatCAD method, which
utilizes large language models (LLMs) such as ChatGPT to enhance the out-
put of multiple CAD networks for medical images, including diagnosis, lesion
segmentation, and report generation networks. The method generates sugges-
tions in the form of a chat dialogue. The authors tested the eﬀectiveness of
the method on a randomly selected set of 300 cases from the MIMIC-CXR
dataset, which included 50 cases each of cardiomegaly, edema, consolidation,
atelectasis, pleural eﬀusion, and no ﬁndings. Compared to CvT2DistilGPT2
and R2GenCMN, chatCAD showed signiﬁcant advantages in RC and F1, while


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and R2GenCMN, chatCAD showed signiﬁcant advantages in RC and F1, while
only performing weaker than R2GenCMN in PR.
Integrating ChatGPT into applications will still present challenges. Firstly,
ChatGPT’s performance may be aﬀected by language barriers or diﬀerences
in terminology between diﬀerent systems. Additionally, ChatGPT’s responses
are not always deterministic, which poses a challenge when integrating with
systems that require precise and reproducible results. Finally, the processing
time of ChatGPT is slow for integration tasks involving time-sensitive data such
as traﬃc, which is a limitation in time-critical environments.
2.2 AI Ethics
Since the advent of ChatGPT, this powerful natural language processing model
has not only brought great convenience to people but also triggered more crisis-
aware thinking. Some researchers have started to hypothesize and study the
potential negative impacts of ChatGPT. This proactive research provides good


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potential negative impacts of ChatGPT. This proactive research provides good
proposals for standardized construction to address future AI abuse issues.
Regarding the evaluation of ChatGPT’s own political and ethical tendencies,
Hartmann et al. [84] used Wahl-O-Mat, one of the most commonly used voting
20


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advice applications in the world, to show ChatGPT political statements from
diﬀerent parties, forcing it to make choices of agree, disagree, or neutral. The
results indicated that ChatGPT has a pro-environment, left-wing liberal ide-
ology, which was also conﬁrmed in the nation-state agnostic political compass
test. Another study (referenced as [85]) examined ChatGPT’s moral standards
by repeatedly asking it diﬀerent versions of the trolley problem, and found that
ChatGPT gave answers with diﬀerent moral orientations, lacking a ﬁrm moral
stance. A subsequent test also found that ChatGPT’s lack of consistency could
aﬀect people’s moral judgments. Additionally, Borji et al. [86] demonstrated
ChatGPT’s inconsistency in reasoning, factual errors, mathematics, coding, and
bias across eleven related aspects. These ﬁndings highlight ChatGPT’s inher-
ent traits and limitations, and people should be aware of their potential impact
when seeking advice from ChatGPT.


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when seeking advice from ChatGPT.
Regarding relevant policies and regulations, Hacker et al. [87] discussed the
nature and rules of large generative AI models, including ChatGPT, which are
rapidly changing the way we communicate, explain, and create. The author
suggested that diﬀerent stakeholders in the value chain should take regulatory
responsibility and deploy four strategies to tailor more comprehensive laws for
the beneﬁt of society. Another study (referenced as [88]) criticized the European
Commission’s proposal on AI responsibility and suggested revising the proposed
AI responsibility framework to ensure eﬀective compensation while promoting
innovation, legal certainty, and sustainable AI regulation. A policy framework
was proposed (referenced as [89]) to customize LLMs, such as ChatGPT, in a so-
cially acceptable and safe manner, emphasizing the need to align large language
models (LLMs) with human preferences.


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models (LLMs) with human preferences.
The political and ethical tendencies of ChatGPT could inﬂuence users’ be-
havior and decision-making to some extent. However, some studies have con-
ducted in-depth research on the use of norms and limitations, which could enable
humans to use ChatGPT more reasonably and safely.
2.3 Evaluation
2.3.1 Comparison of ChatGPT with existing popular models
We use publicly available datasets to comprehensively evaluate the strengths
and limitations of ChatGPT. Reference [90] evaluates the technical performance
of ChatGPT in multitask, multilingual, and multimodal aspects based on 23
standard public datasets and newly designed multimodal datasets, including
eight diﬀerent common natural language processing application tasks. The ex-
perimental results show that, in terms of multitasking, ChatGPT outperforms
various state-of-the-art zero-shot learning large language models in most tasks,


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various state-of-the-art zero-shot learning large language models in most tasks,
and even outperforms ﬁne-tuned task-speciﬁc models in some individual tasks.
In terms of multilingualism, we found that ChatGPT cannot be applied to low-
resource languages because it cannot understand the language and generate
translations for that language. In terms of multimodality, ChatGPT’s ability is
still basic compared to specialized language-visual models.
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In terms of stability, reference [91] concludes that ChatGPT’s performance is
always lower than SOTA, the current state-of-the-art model, in almost all tasks.
This means that as a general model, ChatGPT has never reached the level of the
best existing models. Experimental data shows that the average quality of the
SOTA model is 73.7%, while the average quality of the ChatGPT model is only
56.5%. At the same time, ChatGPT’s stability is poor: the standard deviation
of its performance is 23.3%, while the SOTA model’s standard deviation is
only 16.7%. This non-deterministic behavior exhibited by ChatGPT could be
a serious drawback in some problems.
Similarly, Qin et al. [92] conducted a comprehensive evaluation of whether
ChatGPT is a qualiﬁed general natural language processing task solver. The ex-
periment analyzed ChatGPT’s zero-shot learning ability based on 20 commonly
used public datasets covering 7 representative task categories. Below, we will


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used public datasets covering 7 representative task categories. Below, we will
analyze ChatGPT’s performance on each task:
In terms of reasoning tasks, ChatGPT performs average on mathematical
symbol, commonsense causal, and logical reasoning tasks, but performs well
in arithmetic reasoning [92]. That is to say, ChatGPT’s abilities vary among
diﬀerent types of reasoning tasks. In terms of logical reasoning, ChatGPT’s
deductive and abductive reasoning are superior to inductive reasoning, while
in other reasoning tasks, such as analogy, causal and commonsense reasoning,
ChatGPT performs well [90].
In terms of sentiment analysis task, ChatGPT performs similarly to GPT-3.5
and bert-style models [92, 93]. However, according to literature [91], ChatGPT
has losses not exceeding 25% on most tasks, except for three relatively sub-
jective emotion perception tasks where it performs poorly. If we remove these
tasks to calculate the average quality of the two models, we ﬁnd that the SOTA


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tasks to calculate the average quality of the two models, we ﬁnd that the SOTA
method has an average quality of 80%, while the ChatGPT method has an av-
erage quality of 69.7%. That is to say, ChatGPT performs well on all tasks
except for emotion-related tasks, and can handle most of the problems we con-
sider. However, overall, its performance is lower than the SOTA model based
on experimental data, but the diﬀerence between the two is not very large.
In other tasks, according to literature [92], ChatGPT performs well in nat-
ural language inference, i.e., the task of inferring sentence relationships, and its
performance on this task is signiﬁcantly better than all bert-style models [93].
However, while ChatGPT performs well on inference tasks, it may produce some
self-contradictory or unreasonable responses, which is its potential limitation.
In question-answering, dialogue, and summarization tasks, ChatGPT performs


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In question-answering, dialogue, and summarization tasks, ChatGPT performs
better than the GPT-3.5 model [92], especially in the question-answering task,
where its performance is comparable to bert-style models [93]. Therefore, we
have demonstrated that ChatGPT is a qualiﬁed general-purpose model.
However, ChatGPT also has limitations in many aspects. Firstly, it lacks the
ability to handle non-textual semantic reasoning tasks such as mathematical,
temporal, and spatial reasoning, and it performs poorly in multi-hop reasoning
[90]. Secondly, ChatGPT is not good at solving named entity recognition tasks
[92]. Furthermore, ChatGPT performs poorly in handling tasks involving nega-
tive connotations and neutral similarity [93]. Finally, these conclusions indicate
22


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that, like other large pre-trained language models, ChatGPT has limitations in
completing complex reasoning tasks.
In summary, ChatGPT’s zero-shot performance is comparable to ﬁne-tuned
bert and GPT-3.5 models, and with the help of advanced prompting strategies,
ChatGPT can demonstrate better comprehension abilities. However, it still
cannot outperform the current SOTA models.
2.3.2 The possibility of using ChatGPT for plagiarism and cheating
In response to the possibility of ChatGPT being used for plagiarism and cheat-
ing, Zhou et al. [94] reﬂected on the current state of development of artiﬁcial in-
telligence like ChatGPT. As ChatGPT becomes increasingly easy to obtain and
scalable in text generation, there is a high likelihood that these technologies will
be used for plagiarism, including scientiﬁc literature and news sources, posing
a great threat to the credibility of various forms of news media and academic


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a great threat to the credibility of various forms of news media and academic
articles. Some scholars are concerned that the end of paper as a meaningful
evaluation tool may be approaching [95, 96], as ChatGPT can easily generate
persuasive paragraphs, chapters, and papers on any given topic. Additionally,
it will exacerbate plagiarism issues in many ﬁelds such as education, medicine,
and law [10], and may be used for cheating in academic exams [97]. Deﬁnitional
recognition technology is a relatively eﬀective method for detecting plagiarism,
and the deﬁnitional typology proposed in [94] can alleviate people’s concerns by
being used to construct new datasets. Susnjak [97] proposed a solution to the
possibility of large language models like ChatGPT being used for exam cheat-
ing: guiding ChatGPT to generate some critical thinking problems through
questioning, then providing answers and critically evaluating them. Analysis of


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questioning, then providing answers and critically evaluating them. Analysis of
ChatGPT shows that it exhibits critical thinking, can generate highly realistic
text in terms of accuracy, relevance, depth, breadth, logic, persuasiveness, and
originality. Therefore, educators must be aware of the possibility of ChatGPT
being used for exam cheating and take measures to combat cheating behavior
to ensure the fairness of online exams.
2.3.3 Feedback from ChatGPT users
In response to feedback from ChatGPT users, Haque et al. [98] conducted a
mixed-methods study using 10,732 early ChatGPT user tweets. The authors
extracted Twitter data using Python and Twitter API and constructed the
ChatGPTTweet dataset, which contains 18k tweets. For each tweet, the au-
thors collected information on text content, user location, occupation, veriﬁca-
tion status, date of publication, and tags. Based on this dataset, the authors
studied the characteristics of early ChatGPT users, discussion topics related to


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studied the characteristics of early ChatGPT users, discussion topics related to
ChatGPT on Twitter, and the sentiment of Twitter users toward ChatGPT.
For RQ1, the authors found that early ChatGPT users had a diverse and wide
range of occupational backgrounds and geographical locations. For RQ2, the
authors identiﬁed nine topics related to ChatGPT, including its impact on soft-
ware development, entertainment and creativity, natural language processing,
23


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education, chatbot intelligence, business development, search engines, question-
answering tests, and future careers and opportunities. For RQ3, most early
users expressed positive sentiment toward topics such as software development
and creativity, while only a few expressed concern about the potential misuse
of ChatGPT.
2.3.4 Adverse eﬀects of ChatGPT on users
Regarding the negative eﬀects of ChatGPT on users, Luan et al. [99] studied
the psychological principles of ChatGPT, delved into the factors that attract
users’ attention, and revealed the impact of these factors on future learning.
In the post-pandemic era, teachers and students are both facing uncertainty
in the teaching process and job pressures. Under these common constraints of
education and employment, educators and students must re-evaluate current
educational methods and outcomes, as well as students’ future career devel-
opment. Through question-and-answer exchanges with ChatGPT, people can


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opment. Through question-and-answer exchanges with ChatGPT, people can
easily obtain appropriate solutions or key information, thereby enhancing their
motivation, eliminating anxiety in learning, improving interest, and achieving
psychological satisfaction. Subhash et al. [100] explored whether large language
models have the ability to reverse user preferences. With the development of
pre-trained large language models, people are increasingly concerned about the
ability of these models to inﬂuence, persuade, and potentially manipulate user
preferences in extreme cases. Therefore, the literature [100] roughly qualita-
tively analyzed that adversarial behavior does lead to potential changes in user
preferences and behaviors in dialogue systems. If we want to further quanti-
tatively analyze the ability of large language models in this regard, additional
statistical summary techniques need to be used for future research.
3 Discussion
3.1 Limitations


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statistical summary techniques need to be used for future research.
3 Discussion
3.1 Limitations
Despite the remarkable capabilities of ChatGPT and GPT-4, it still faces certain
limitations. Some of these limitations include:
Outdated Knowledge
The current models are trained on historical data (up to 2021), thereby
lacking real-time comprehension of current aﬀairs. This is a critical concern
in today’s information-explosion era, as the reliability of prior knowledge bases
progressively diminishes, potentially yielding inaccurate responses, especially in
rapidly evolving domains such as jurisprudence and technology. Additionally,
these models are incapable of fact-checking while the training data is composed
of content from various sources, some of which may be unreliable, which may
result in seemingly plausible yet nonsensical responses.
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Insuﬃcient Understanding
While these models can interpret the majority of inquiries and contextual
situations, they occasionally encounter comprehension biases when addressing
ambiguous or contextually complex queries. Furthermore, in certain specialized
ﬁelds, the abundance of unique abbreviation exacerbates the models’ under-
standing challenges, resulting in incorrect and vacuous responses.
Energy Consumption
Throughout the training and inference stages, these large-scale models re-
quire signiﬁcant computational resources and electrical power, resulting in ele-
vated energy consumption and signiﬁcant carbon emissions. Consequently, this
restricts their deployment and practical applications.
Malicious Usage
Despite OpenAI implementing a series of restrictions to mitigate model tox-
icity, instances of users evading these constraints through meticulously designed
prompts have emerged, inducing the model to produce unhealthy content or
even using it for illicit commercial purposes.


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even using it for illicit commercial purposes.
Bias and Discrimination
Due to the inﬂuence of pre-training data, the models exhibit biases in polit-
ical, ideological, and other areas. The application of LLMs in public domains,
such as education and publicity, should be approached with extreme caution.
Privacy and Data Security
Concurrent with the expansion of users, protecting user privacy and data
security becomes increasingly important. In fact, ChatGPT was banned in
Italy in early April due to privacy concerns. This is particularly relevant given
the models’ extensive collection of personal information and preferences during
interactions, and as future multimodal models, such as GPT-4, may frequently
require users to upload private photos.
3.2 Future Directions
In forthcoming research, the development of models based on ChatGPT and
GPT-4 may focus on addressing these limitations to enhance their practical
applications.


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GPT-4 may focus on addressing these limitations to enhance their practical
applications.
Primarily, researchers should continue to work on reﬁning model training
methodologies while ﬁltering pre-training data to minimize the presence of mis-
leading information in the model’s knowledge base, thereby obtaining accurate
responses. Concurrently, it is crucial to emphasize training approaches that
economize computational resources, thereby mitigating costs and broadening
potential application scenarios.
Moreover, the advancements in context-awareness and disambiguation tech-
nologies are anticipated to facilitate enhanced comprehension of complex queries
25


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by models, improving the accuracy, relevance, and context-awareness of AI-
generated content. Integrating real-time data streams can also keep these mod-
els in sync with current events and trends, enabling them to provide up-to-date
information such as live traﬃc, weather, and stock updates.
Additionally, developers should engage in interdisciplinary collaboration with
specialists from diverse domains, including policy-making, jurisprudence, and
sociology, with the objective of formulating standard and ethical frameworks
for LLM development, deployment, and utilization, thereby alleviating poten-
tial harmful consequences. In terms of public awareness and education, manda-
tory awareness training should be implemented prior to large-scale public de-
ployment and application to increase public awareness of LLM capabilities and
limitations while promoting responsible and informed utilization, especially in
industries such as K-12 education and journalism.


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industries such as K-12 education and journalism.
Finally, the inﬂuence of ChatGPT and GPT-4 should not be limited to
just the NLP ﬁeld. They also show promising prospects in the areas of com-
puter vision, brain-inspired AI, and robotics. These models exhibit a capacity
for learning and comprehension comparable with human-level intelligence, po-
sitioning them as a pivotal component in the development of artiﬁcial general
intelligence (AGI)[101]. Their ability to facilitate seamless interactions between
humans and robots paves the way for the execution of more complex tasks.
The remarkable capacity of zero-shot in-context learning of these models en-
ables quick adaptation to new tasks without the requirement for labeled data
for ﬁne-tuning, which is a critical challenge in ﬁelds like medical informatics[102]
and robotics[103] where the availability of labeled data is commonly limited or
non-existent.
4 Conclusion


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non-existent.
4 Conclusion
This review paper provides a comprehensive survey of ChatGPT and GPT-4,
highlighting their potential applications and signiﬁcant contributions to the ﬁeld
of natural language processing. The ﬁndings of this study reveal that the interest
in these models is growing rapidly, and they have shown considerable potential
for application across a wide range of domains. One key factor contributing
to the success of ChatGPT and GPT-4 is their ability to perform large-scale
pre-training, which captures knowledge from the vast expanse of the internet,
allowing the models to learn from a massive amount of data. The integration of
Reinforcement Learning from Human Feedback (RLHF) has further enhanced
the model’s adaptability and performance, making it highly eﬃcient in process-
ing natural language. This study has also identiﬁed several potential ethical
concerns related to the development and use of ChatGPT and GPT-4. For in-


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concerns related to the development and use of ChatGPT and GPT-4. For in-
stance, there are concerns about the generation of biased or harmful content,
privacy violations, and the potential for misuse of the technology. It is crucial
to address these concerns and ensure that ChatGPT and GPT-4 are developed
and used in a responsible and ethical manner. Furthermore, the results of this
study demonstrate that there is signiﬁcant potential for ChatGPT and GPT-4
26


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to be applied in a range of domains, including education, history, mathematics,
physics, and more. These models can facilitate tasks such as generating sum-
maries, answering questions, and providing personalized recommendations to
users. Overall, the insights presented in this review paper can serve as a useful
guide for researchers and practitioners looking to advance the ﬁeld of natural
language processing. Future research in this ﬁeld should focus on addressing
ethical concerns, exploring new applications, and ensuring the responsible use
of ChatGPT and GPT-4. The potential of these models to revolutionize natural
language processing is enormous, and we look forward to seeing more develop-
ments in this ﬁeld.
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