# 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: 0735af16-f3ba-42a1-bdee-bb6db86ef6df
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":{"keywords":{"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":{}}}},"page":{"int":{"int_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":{}}}},"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":{}}}},"source":{"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":{}}}},"producer":{"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":{}}}},"creationdate":{"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":{}}}},"total_pages":{"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}}}}}}}


# embedding_metadata

key: chroma:document
string_value:
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


key: chroma:document
string_value:
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


key: chroma:document
string_value:
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


key: chroma:document
string_value:
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


key: chroma:document
string_value:
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


key: chroma:document
string_value:
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


key: chroma:document
string_value:
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


key: chroma:document
string_value:
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-


key: chroma:document
string_value:
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-


key: chroma:document
string_value:
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


key: chroma:document
string_value:
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


key: chroma:document
string_value:
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


key: chroma:document
string_value:
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


key: chroma:document
string_value:
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


key: chroma:document
string_value:
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


key: chroma:document
string_value:
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


key: chroma:document
string_value:
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-


key: chroma:document
string_value:
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-


key: chroma:document
string_value:
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-


key: chroma:document
string_value:
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


key: chroma:document
string_value:
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


key: chroma:document
string_value:
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


key: chroma:document
string_value:
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


key: chroma:document
string_value:
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


key: chroma:document
string_value:
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-


key: chroma:document
string_value:
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


key: chroma:document
string_value:
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


key: chroma:document
string_value:
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-


key: chroma:document
string_value:
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


key: chroma:document
string_value:
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-


key: chroma:document
string_value:
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.


key: chroma:document
string_value:
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


key: chroma:document
string_value:
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


key: chroma:document
string_value:
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.
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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-
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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
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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,
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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.
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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
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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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{"author":"","subject":"","total_pages":35,"chroma:document":"entire world wide web, instruction ﬁne-tuning and Reinforcement Learn-\ning from Human Feedback (RLHF) have played signiﬁcant roles in en-\nhancing LLMs’ adaptability and performance. We performed an in-depth\nanalysis of 194 relevant papers on arXiv, encompassing trend analysis,\nword cloud representation, and distribution analysis across various appli-\ncation domains. The ﬁndings reveal a signiﬁcant and increasing interest\nin ChatGPT/GPT-4 research, predominantly centered on direct natural","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","producer":"pdfTeX-1.40.21","page":0,"moddate":"2023-04-05T00:33:07+00:00","page_label":"1","keywords":"","trapped":"/False","creator":"LaTeX with hyperref","creationdate":"2023-04-05T00:33:07+00:00","title":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2"}


created_at: 2026-06-08 08:17:00
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: f5e3f8b6-48d3-47d6-a780-483434d423d0
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{"producer":"pdfTeX-1.40.21","total_pages":35,"moddate":"2023-04-05T00:33:07+00:00","title":"","creator":"LaTeX with hyperref","subject":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","trapped":"/False","page_label":"1","page":0,"author":"","creationdate":"2023-04-05T00:33:07+00:00","keywords":"","chroma:document":"language processing applications, while also demonstrating considerable\npotential in areas ranging from education and history to mathematics,\n∗Co-ﬁrst author\n†Corresponding author: bob ge@snnu.edu.cn\n1\narXiv:2304.01852v1  [cs.CL]  4 Apr 2023"}


created_at: 2026-06-08 08:17:00
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: 20c78b16-6762-4e61-8db9-a1a9ff720a0a
encoding: FLOAT32
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{"creator":"LaTeX with hyperref","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","trapped":"/False","moddate":"2023-04-05T00:33:07+00:00","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","author":"","page_label":"2","page":1,"total_pages":35,"title":"","chroma:document":"medicine, and physics. This study endeavors to furnish insights into Chat-\nGPT’s capabilities, potential implications, ethical concerns, and oﬀer di-\nrection for future advancements in this ﬁeld.\n1 Introduction\nRecent advances in natural language processing (NLP) have led to the devel-\nopment of powerful language models such as the GPT (Generative Pre-trained\nTransformer) series [1, 2, 3, 4, 5], including large language models (LLM) such","keywords":"","producer":"pdfTeX-1.40.21","creationdate":"2023-04-05T00:33:07+00:00","subject":""}


created_at: 2026-06-08 08:17:00
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: eaeb4df4-f808-447e-8410-3efa083b297f
encoding: FLOAT32
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{"author":"","moddate":"2023-04-05T00:33:07+00:00","subject":"","total_pages":35,"producer":"pdfTeX-1.40.21","title":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","trapped":"/False","keywords":"","creator":"LaTeX with hyperref","creationdate":"2023-04-05T00:33:07+00:00","page_label":"2","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","chroma:document":"as ChatGPT and GPT-4. These models are pre-trained on vast amounts of\ntext data and have demonstrated exceptional performance in a wide range of\nNLP tasks, including language translation, text summarization, and question-\nanswering. In particular, the ChatGPT model has demonstrated its potential\nin various ﬁelds, including education, healthcare, reasoning, text generation,\nhuman-machine interaction, and scientiﬁc research.\nA key milestone of LLM development is InstructGPT [5], a framework that","page":1}


created_at: 2026-06-08 08:17:00
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: a6353f45-feb1-4e1a-829e-4b604ec32580
encoding: FLOAT32
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{"creationdate":"2023-04-05T00:33:07+00:00","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","keywords":"","subject":"","title":"","page":1,"page_label":"2","creator":"LaTeX with hyperref","moddate":"2023-04-05T00:33:07+00:00","author":"","trapped":"/False","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","total_pages":35,"chroma:document":"allows for instruction ﬁne-tuning of a pre-trained language model based on Re-\ninforcement Learning from Human Feedback (RLHF) [6, 5]. This framework\nenables an LLM to adapt to a wide range of NLP tasks, making it highly ver-\nsatile and ﬂexible by leveraging human feedback. RLHF enables the model to\nalign with human preferences and human values, which signiﬁcantly improves\nfrom large language models that are solely trained text corpora through unsu-","producer":"pdfTeX-1.40.21"}


created_at: 2026-06-08 08:17:00
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: 62d5494f-5aa6-48f6-ae94-a28b38e01815
encoding: FLOAT32
metadata:
{"creator":"LaTeX with hyperref","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","moddate":"2023-04-05T00:33:07+00:00","creationdate":"2023-04-05T00:33:07+00:00","subject":"","chroma:document":"pervised pre-training. ChatGPT is a successor to InstructGPT. Since its release\nin December 2022, ChatGPT has been equipped with these advanced develop-\nments, leading to impressive performances in various downstream NLP tasks\nsuch as reasoning and generalized text generation. These unprecedented NLP\ncapabilities spur applications in diverse domains such as education, healthcare,\nhuman-machine interaction, medicine and scientiﬁc research. ChatGPT has re-","total_pages":35,"keywords":"","trapped":"/False","page":1,"page_label":"2","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","title":"","author":"","producer":"pdfTeX-1.40.21"}


created_at: 2026-06-08 08:17:00
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: 2b896006-5287-4016-887a-11ac0c5da67c
encoding: FLOAT32
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{"creationdate":"2023-04-05T00:33:07+00:00","author":"","page_label":"2","moddate":"2023-04-05T00:33:07+00:00","keywords":"","title":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","subject":"","trapped":"/False","total_pages":35,"producer":"pdfTeX-1.40.21","chroma:document":"ceived widespread attention and interest, leading to an increasing number of\napplications and research that harness its exceeding potential. The open release\nof the multi-modal GPT-4 model further expands the horizon of large language\nmodels and empowers exciting developments that involve diverse data beyond\ntext.\nThe purpose of this paper is to provide a comprehensive survey of the existing\nresearch on ChatGPT and its potential applications in various ﬁelds. To achieve","page":1,"creator":"LaTeX with hyperref"}


created_at: 2026-06-08 08:17:00
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: e4ae68ab-5f3e-409e-aacc-ce6f4cc127bb
encoding: FLOAT32
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{"source":"./data/Summary of ChatGPTGPT-4 Research.pdf","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","keywords":"","creationdate":"2023-04-05T00:33:07+00:00","page_label":"2","author":"","subject":"","page":1,"moddate":"2023-04-05T00:33:07+00:00","total_pages":35,"title":"","producer":"pdfTeX-1.40.21","trapped":"/False","chroma:document":"this goal, we conducted a thorough analysis of papers related to ChatGPT in\nthe arXiv repository. As of April 1st, 2023, there are a total of 194 papers\nmentioning ChatGPT on arXiv. In this study, we conducted a trend analysis of\nthese papers and generated a word cloud to visualize the commonly used terms.\nAdditionally, we also examined the distribution of the papers across various\nﬁelds and presented the corresponding statistics. Figure 1 displays the daily","creator":"LaTeX with hyperref"}


created_at: 2026-06-08 08:17:00
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: 713aeb75-033e-4b54-b194-22471f4a3878
encoding: FLOAT32
metadata:
{"title":"","creator":"LaTeX with hyperref","creationdate":"2023-04-05T00:33:07+00:00","subject":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","page":1,"page_label":"2","total_pages":35,"author":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","producer":"pdfTeX-1.40.21","keywords":"","trapped":"/False","moddate":"2023-04-05T00:33:07+00:00","chroma:document":"submission trend of papers related to ChatGPT, indicating a growing interest\nin this ﬁeld. Figure 2 illustrates the word cloud analysis of all the papers. We\ncan observe that the current research is primarily focused on natural language\nprocessing, but there is still signiﬁcant potential for research in other ﬁelds such\n2"}


created_at: 2026-06-08 08:17:00
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: db754dd4-24e8-4325-a85f-ad669e4f2cd6
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{"total_pages":35,"title":"","creator":"LaTeX with hyperref","trapped":"/False","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","chroma:document":"Figure 1: The number of papers submitted by researchers per day.\n3","moddate":"2023-04-05T00:33:07+00:00","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","page":2,"creationdate":"2023-04-05T00:33:07+00:00","keywords":"","author":"","subject":"","page_label":"3","producer":"pdfTeX-1.40.21"}


created_at: 2026-06-08 08:17:00
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: 7a8f7b6c-b7f5-4d47-ac61-c36b5bfbbaa6
encoding: FLOAT32
metadata:
{"moddate":"2023-04-05T00:33:07+00:00","subject":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","creationdate":"2023-04-05T00:33:07+00:00","total_pages":35,"source":"./data/Summary of ChatGPTGPT-4 Research.pdf","chroma:document":"Figure 2: Word cloud analysis of all the 194 papers.\nas education and history. This is further supported by Figure 3, which displays\nthe distribution of submitted papers across various ﬁelds, highlighting the need\nfor more research and development in these areas.\nThis paper aims to shed light on the promising capabilities of ChatGPT\nand provide insight into its potential impact in the future, including ethical\nconsiderations. Through this survey, we hope to provide insights into how these","trapped":"/False","producer":"pdfTeX-1.40.21","title":"","author":"","creator":"LaTeX with hyperref","keywords":"","page":3,"page_label":"4"}


created_at: 2026-06-08 08:17:00
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: 03f70cbd-630c-49ae-b9ce-14fbbee234dc
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{"page_label":"4","chroma:document":"models can be improved and extended in the future. In section 2, we will\nreview the existing work related to ChatGPT, including its applications, ethical\nconsiderations, and evaluation. In addition to discussing the current state of\nresearch related to ChatGPT, we will also explore its limitations in section 3.\nFurthermore, we will provide guidance on future directions for language model\ndevelopment.\n2 Related work of ChatGPT","author":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","trapped":"/False","total_pages":35,"title":"","moddate":"2023-04-05T00:33:07+00:00","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","keywords":"","producer":"pdfTeX-1.40.21","creationdate":"2023-04-05T00:33:07+00:00","page":3,"creator":"LaTeX with hyperref","subject":""}


created_at: 2026-06-08 08:17:00
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: 36dbf07a-1264-4fe0-83f8-a89dba4d8c88
encoding: FLOAT32
metadata:
{"moddate":"2023-04-05T00:33:07+00:00","subject":"","total_pages":35,"creator":"LaTeX with hyperref","keywords":"","author":"","page":3,"producer":"pdfTeX-1.40.21","page_label":"4","title":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","trapped":"/False","chroma:document":"development.\n2 Related work of ChatGPT\nIn this section, we review the latest research related to the application, ethics,\nand evaluation of ChatGPT.\n2.1 Application of ChatGPT\n2.1.1 Question And Answering\nIn the education ﬁeld\nChatGPT is commonly used for question and answers testing in the edu-\ncation sector. Users can use ChatGPT to learn, compare and verify answers\nfor diﬀerent academic subjects such as physics, mathematics, and chemistry,\n4","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","creationdate":"2023-04-05T00:33:07+00:00"}


created_at: 2026-06-08 08:17:00
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: a2044089-4ee3-42de-a584-4dea4f7ecb1b
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{"subject":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","creationdate":"2023-04-05T00:33:07+00:00","moddate":"2023-04-05T00:33:07+00:00","page_label":"5","trapped":"/False","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","keywords":"","title":"","total_pages":35,"author":"","producer":"pdfTeX-1.40.21","chroma:document":"Figure 3: The distribution of submitted papers across various ﬁelds.\n5","page":4,"creator":"LaTeX with hyperref"}


created_at: 2026-06-08 08:17:00
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: a47edc9b-21f7-421f-b6f4-f879d832c4d4
encoding: FLOAT32
metadata:
{"page_label":"6","producer":"pdfTeX-1.40.21","page":5,"title":"","author":"","total_pages":35,"creator":"LaTeX with hyperref","keywords":"","trapped":"/False","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","subject":"","moddate":"2023-04-05T00:33:07+00:00","creationdate":"2023-04-05T00:33:07+00:00","chroma:document":"and/or conceptual subjects such as philosophy and religion. Additionally, users\ncan ask open-ended and analytical questions to understand the capabilities of\nChatGPT.\nIn the ﬁeld of mathematics, Frieder et al. [7] constructed the GHOSTS nat-\nural language dataset, which consists of graduate-level math test questions.\nThe authors tested ChatGPT’s math abilities on the GHOSTS dataset us-\ning a question-and-answer format and evaluated it according to ﬁne-grained","source":"./data/Summary of ChatGPTGPT-4 Research.pdf"}


created_at: 2026-06-08 08:17:00
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: 8b4356de-eceb-4944-ac48-08e50602e601
encoding: FLOAT32
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{"chroma:document":"standards.In the Grad Text dataset, which covers simple set theory and logic\nproblems, ChatGPT performed the best. However, in the Olympiad-Problem-\nSolving dataset, ChatGPT performed poorly, receiving only two 4-point scores\n(out of a total of 5), with the majority of scores being 2 points. In the Holes-\nin-Proofs dataset, ChatGPT received the lowest score of 1 point. In the MATH\ndataset, ChatGPT only scored impressively in 26% of cases. These results sug-","keywords":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","moddate":"2023-04-05T00:33:07+00:00","producer":"pdfTeX-1.40.21","trapped":"/False","page":5,"title":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","subject":"","author":"","page_label":"6","creationdate":"2023-04-05T00:33:07+00:00","creator":"LaTeX with hyperref","total_pages":35}


created_at: 2026-06-08 08:17:00
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: b1158a21-69d6-4837-a44f-4cae8e7f517d
encoding: FLOAT32
metadata:
{"title":"","page":5,"producer":"pdfTeX-1.40.21","subject":"","trapped":"/False","creator":"LaTeX with hyperref","total_pages":35,"source":"./data/Summary of ChatGPTGPT-4 Research.pdf","chroma:document":"gest that ChatGPT’s math abilities are clearly lower than those of ordinary\nmath graduate students. Although ChatGPT can generally understand math\nproblems, it fails to provide the correct solutions. Pardos et al. [8] used the\nOpen Adaptive Tutoring system (OATutor) to investigate whether prompts gen-\nerated by ChatGPT were helpful for learning algebra, with 77 participants from\nMechanical Turk taking part in the experiment. The experiment used ques-","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","author":"","page_label":"6","moddate":"2023-04-05T00:33:07+00:00","creationdate":"2023-04-05T00:33:07+00:00","keywords":""}


created_at: 2026-06-08 08:17:00
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: 9ef56cc9-9db4-4fa2-a4e6-5b37bb7b2e44
encoding: FLOAT32
metadata:
{"moddate":"2023-04-05T00:33:07+00:00","subject":"","creator":"LaTeX with hyperref","title":"","page":5,"creationdate":"2023-04-05T00:33:07+00:00","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","author":"","page_label":"6","chroma:document":"tions from OpenStax’s Elementary and Intermediate Algebra textbooks. These\nparticipants were randomly assigned to either a control group (with manual\nprompts) or an experimental group (with ChatGPT prompts). For each ques-\ntion in both courses, the authors obtained answers from ChatGPT through a\nquestion-and-answer format and evaluated scores according to three criteria:\nChatGPT provided an answer, the answer was correct, and inappropriate lan-","producer":"pdfTeX-1.40.21","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","keywords":"","trapped":"/False","total_pages":35}


created_at: 2026-06-08 08:17:00
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: 2d1fe1f3-8d94-4366-8cac-8c49665ec6a2
encoding: FLOAT32
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{"subject":"","total_pages":35,"trapped":"/False","creationdate":"2023-04-05T00:33:07+00:00","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","author":"","producer":"pdfTeX-1.40.21","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","page":5,"creator":"LaTeX with hyperref","chroma:document":"guage was not used in the answer. The study found that 70% of prompts gener-\nated by ChatGPT passed manual quality checks, and both humans and Chat-\nGPT produced positive learning gains. However, the scores of human prompts\nranged from 74.59% to 84.32%, signiﬁcantly higher than those of ChatGPT\nprompts. Shakarian et al. [9] studied the performance of ChatGPT on math\nword problems (MWPs), using the DRAW-1K dataset for experimentation. The","moddate":"2023-04-05T00:33:07+00:00","title":"","keywords":"","page_label":"6"}


created_at: 2026-06-08 08:17:00
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: 3ced5a85-986a-419b-ae9e-6e3cf3e7a712
encoding: FLOAT32
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{"page":5,"author":"","creationdate":"2023-04-05T00:33:07+00:00","total_pages":35,"moddate":"2023-04-05T00:33:07+00:00","chroma:document":"dataset consists of 1000 MWPs and their answers, along with algebraic equa-\ntion templates for solving such problems. The authors used the idea of machine\nlearning introspection and built performance prediction models using random\nforests and XGBoost, and evaluated them on the dataset using ﬁve-fold cross-\nvalidation. ChatGPT’s accuracy increased from an initial 34% to a ﬁnal 69%,\nwhile its recall increased from an initial 41% to a ﬁnal 83%. The authors also","trapped":"/False","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","subject":"","title":"","producer":"pdfTeX-1.40.21","page_label":"6","creator":"LaTeX with hyperref","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","keywords":""}


created_at: 2026-06-08 08:17:00
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: 96874d55-360a-4bdc-91f6-e86071a602e0
encoding: FLOAT32
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{"keywords":"","chroma:document":"found that ChatGPT’s failure rate decreased from an initial 84% to a ﬁnal\n20%, indicating that performance can vary greatly depending on speciﬁc job\nrequirements.\nIn the ﬁeld of physics, Lehnert et al. [10] explored the capabilities and limita-\ntions of ChatGPT by studying how it handles obscure physics topics such as the\nswamp land conjecture in string theory. The experimental dialogue began with\nbroader and more general questions in the ﬁeld of string theory before narrowing","producer":"pdfTeX-1.40.21","moddate":"2023-04-05T00:33:07+00:00","title":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","page":5,"creator":"LaTeX with hyperref","page_label":"6","trapped":"/False","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","subject":"","total_pages":35,"creationdate":"2023-04-05T00:33:07+00:00","author":""}


created_at: 2026-06-08 08:17:00
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: 9f00bdbd-8716-4315-a5b3-55d0c1ce4d2e
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{"keywords":"","subject":"","page":5,"creator":"LaTeX with hyperref","producer":"pdfTeX-1.40.21","chroma:document":"down to speciﬁc swamp land conjectures and examining ChatGPT’s understand-\ning of them. The study found that ChatGPT could deﬁne and explain diﬀerent\n6","author":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","page_label":"6","title":"","total_pages":35,"trapped":"/False","creationdate":"2023-04-05T00:33:07+00:00","moddate":"2023-04-05T00:33:07+00:00","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2"}


created_at: 2026-06-08 08:17:00
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: 577fcc4a-1e22-4392-a565-632af2b19904
encoding: FLOAT32
metadata:
{"keywords":"","creationdate":"2023-04-05T00:33:07+00:00","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","title":"","total_pages":35,"moddate":"2023-04-05T00:33:07+00:00","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","producer":"pdfTeX-1.40.21","chroma:document":"concepts in various styles, but was not eﬀective in truly connecting various con-\ncepts. It would conﬁdently provide false information and fabricate statements\nwhen necessary, indicating that ChatGPT cannot truly create new knowledge\nor establish new connections. However, in terms of identifying analogies and\ndescribing abstract concepts of visual representation, ChatGPT can cleverly\nuse language. Kortemeyer et al. [11] evaluated ChatGPT’s ability to answer","page_label":"7","creator":"LaTeX with hyperref","page":6,"subject":"","author":"","trapped":"/False"}


created_at: 2026-06-08 08:17:00
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
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created_at: 2026-06-08 08:17:00
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: 740a8a47-fd29-4b7a-9816-b7677ab21d44
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{"total_pages":35,"author":"","trapped":"/False","page_label":"7","producer":"pdfTeX-1.40.21","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","title":"","page":6,"creator":"LaTeX with hyperref","moddate":"2023-04-05T00:33:07+00:00","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","keywords":"","creationdate":"2023-04-05T00:33:07+00:00","chroma:document":"racy in answering physics concept problems related to kinematics and Newtonian\nmechanics in the ﬁrst semester of college physics. The FCI covers topics such\nas kinematics, projectile motion, free fall, circular motion, and Newton’s laws.\nThe study included data from 415 students who took the FCI at the end of the\nsemester, with an average score of 56%, while ChatGPT scored approximately\nbetween 50% to 65%. The authors demonstrated that ChatGPT’s performance","subject":""}


created_at: 2026-06-08 08:17:00
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: df17eb9c-d819-44d2-bbaf-93dbc7668b48
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{"trapped":"/False","producer":"pdfTeX-1.40.21","subject":"","moddate":"2023-04-05T00:33:07+00:00","author":"","page_label":"7","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","page":6,"ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","chroma:document":"in physics learning can reach or even exceed the average level of a semester of\ncollege physics.\nIn the medical ﬁeld\nChatGPT’s question-answering capabilities can also be applied in the med-\nical ﬁeld, such as for answering medical questions from patients or assisting\nhealthcare professionals in diagnosing diseases. Nov et al. [13] evaluated the fea-\nsibility of using ChatGPT for patient-doctor communication. The experiment","creationdate":"2023-04-05T00:33:07+00:00","title":"","keywords":"","creator":"LaTeX with hyperref","total_pages":35}


created_at: 2026-06-08 08:17:00
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: 91709887-7de0-4acc-b15d-dceb86bb9c8a
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{"producer":"pdfTeX-1.40.21","trapped":"/False","keywords":"","creationdate":"2023-04-05T00:33:07+00:00","page_label":"7","subject":"","page":6,"title":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","author":"","chroma:document":"extracted 10 representative patient-doctor interactions from EHR, placed the\npatient’s questions in ChatGPT, and asked ChatGPT to respond using roughly\nthe same number of words as the doctor’s response. Each patient’s question was\nanswered by either the doctor or ChatGPT, and the patient was informed that\n5 were answered by the doctor and 5 were generated by ChatGPT, and was\nasked to correctly identify the source of the response. The results of the exper-","creator":"LaTeX with hyperref","total_pages":35,"moddate":"2023-04-05T00:33:07+00:00","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2"}


created_at: 2026-06-08 08:17:00
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: acb8ac96-d3c0-430e-8810-87d71a471744
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{"page":6,"total_pages":35,"author":"","creator":"LaTeX with hyperref","moddate":"2023-04-05T00:33:07+00:00","trapped":"/False","chroma:document":"iment showed that the probability of correctly identifying ChatGPT’s response\nwas 65.5%, while the probability of correctly identifying the doctor’s response\nwas 65.1%. In addition, the experiment found that the patient’s response to\nthe trustworthiness of ChatGPT’s function was weakly positive (average Likert\nscore: 3.4), and trust decreased as the complexity of health-related tasks in\nthe questions increased. ChatGPT’s responses to patient questions were only","creationdate":"2023-04-05T00:33:07+00:00","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","title":"","subject":"","producer":"pdfTeX-1.40.21","page_label":"7","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","keywords":""}


created_at: 2026-06-08 08:17:00
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: 7efd995a-ddcf-479f-8076-058e9d91ba9a
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{"chroma:document":"slightly diﬀerent from those of doctors, but people seem to trust ChatGPT to\nanswer low-risk health questions, while for complex medical questions, people\nstill tend to trust the doctor’s responses and advice.\nTu et al. [14] explored the causal discovery ability of ChatGPT in the diag-\nnosis of neuropathic pain. Causal relationship discovery aims to reveal potential\nunknown causal relationships based purely on observed data [15]. The experi-","author":"","page":6,"moddate":"2023-04-05T00:33:07+00:00","creationdate":"2023-04-05T00:33:07+00:00","keywords":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","subject":"","trapped":"/False","producer":"pdfTeX-1.40.21","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","title":"","creator":"LaTeX with hyperref","total_pages":35,"page_label":"7"}


created_at: 2026-06-08 08:17:00
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: 852bb41e-9737-4c8d-926b-a8fff3a1e545
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{"ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","total_pages":35,"keywords":"","chroma:document":"mental results found that ChatGPT has some limitations in understanding new\n7","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","title":"","creator":"LaTeX with hyperref","page":6,"subject":"","producer":"pdfTeX-1.40.21","creationdate":"2023-04-05T00:33:07+00:00","trapped":"/False","moddate":"2023-04-05T00:33:07+00:00","page_label":"7","author":""}


created_at: 2026-06-08 08:17:00
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: 03904735-0bd8-4ba6-8ab9-46f33655d262
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{"title":"","subject":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","page":7,"keywords":"","producer":"pdfTeX-1.40.21","trapped":"/False","total_pages":35,"page_label":"8","creator":"LaTeX with hyperref","creationdate":"2023-04-05T00:33:07+00:00","author":"","moddate":"2023-04-05T00:33:07+00:00","chroma:document":"knowledge and concepts beyond the existing textual training data corpus, that\nis, it only understands language commonly used to describe situations and not\nunderlying knowledge. In addition, its performance consistency and stability are\nnot high, as the experiment observed that it would provide diﬀerent answers for\nthe same question under multiple inquiries. However, despite the many limita-\ntions of ChatGPT, we believe that it has a great opportunity to improve causal\nrelationship research."}


created_at: 2026-06-08 08:17:00
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: cd2cf4ca-db2b-43f4-928a-7e36eb478295
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{"chroma:document":"relationship research.\nIn the other ﬁeld\nGuo et al. [16] attempted to apply ChatGPT in the ﬁeld of communication,\nspeciﬁcally using ChatGPT for ordered importance semantic communication,\nwhere ChatGPT plays the role of an intelligent consulting assistant that can\nreplace humans in identifying the semantic importance of words in messages and\ncan be directly embedded into the current communication system. For a message\nto be transmitted, the sender ﬁrst utilizes ChatGPT to output the semantic","producer":"pdfTeX-1.40.21","moddate":"2023-04-05T00:33:07+00:00","trapped":"/False","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","author":"","subject":"","total_pages":35,"keywords":"","creator":"LaTeX with hyperref","title":"","creationdate":"2023-04-05T00:33:07+00:00","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","page_label":"8","page":7}


created_at: 2026-06-08 08:17:00
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: 083f0b39-eef2-42c9-83f9-87a9fd6aea06
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{"author":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","trapped":"/False","creationdate":"2023-04-05T00:33:07+00:00","moddate":"2023-04-05T00:33:07+00:00","page_label":"8","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","producer":"pdfTeX-1.40.21","page":7,"creator":"LaTeX with hyperref","keywords":"","chroma:document":"importance order of each word. Then, the transmitter executes an unequal error\nprotection transmission strategy based on the importance order to make the\ntransmission of important words in the message more reliable. The experimental\nresults show that the error rate and semantic loss of important words measured\nin the communication system embedded with ChatGPT are much lower than\nthose of existing communication schemes, indicating that ChatGPT can protect","title":"","subject":"","total_pages":35}


created_at: 2026-06-08 08:17:00
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: ab45470c-c0b6-41fc-9770-a371ed767e58
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{"page":7,"author":"","chroma:document":"important words well and make semantic communication more reliable.\nWang et al. [17] studied the eﬀectiveness of ChatGPT in generating high-\nquality Boolean queries for systematic literature search. They designed a wide\nrange of prompts and investigated these tasks on more than 100 systematic\nreview topics. In the end, queries generated by ChatGPT achieved higher accu-\nracy compared to the currently most advanced query generation methods but","total_pages":35,"title":"","moddate":"2023-04-05T00:33:07+00:00","trapped":"/False","page_label":"8","subject":"","creator":"LaTeX with hyperref","producer":"pdfTeX-1.40.21","creationdate":"2023-04-05T00:33:07+00:00","keywords":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","source":"./data/Summary of ChatGPTGPT-4 Research.pdf"}


created_at: 2026-06-08 08:17:00
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: 17a524a8-b1ad-4ecd-b240-82713e9b8394
encoding: FLOAT32
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{"producer":"pdfTeX-1.40.21","page":7,"author":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","creationdate":"2023-04-05T00:33:07+00:00","moddate":"2023-04-05T00:33:07+00:00","total_pages":35,"page_label":"8","creator":"LaTeX with hyperref","keywords":"","chroma:document":"at the cost of reduced recall. For time-limited rapid reviews, it is often accept-\nable to trade oﬀ higher precision for lower recall. Additionally, ChatGPT can\ngenerate high search accuracy Boolean queries by guiding the prompts. How-\never, it should be noted that when two queries use the same prompts, ChatGPT\ngenerates diﬀerent queries, indicating its limitations in consistency and stabil-\nity. Overall, this study demonstrated the potential of ChatGPT in generating","subject":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","title":"","trapped":"/False"}


created_at: 2026-06-08 08:17:00
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: 07bd230c-16be-45a1-a9d4-06e8cefc2fd0
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{"moddate":"2023-04-05T00:33:07+00:00","total_pages":35,"title":"","subject":"","keywords":"","creator":"LaTeX with hyperref","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","author":"","trapped":"/False","creationdate":"2023-04-05T00:33:07+00:00","producer":"pdfTeX-1.40.21","page_label":"8","page":7,"chroma:document":"eﬀective Boolean queries for systematic literature searches.\n2.1.2 Text Classiﬁcation\nThe purpose of text classiﬁcation is to assign text data to predeﬁned categories.\nThis task is critical for many applications, including sentiment analysis, spam\ndetection, and topic modeling. While traditional machine learning algorithms\nhave been widely used for text classiﬁcation, recent advances in natural lan-\nguage processing have led to the development of more advanced techniques."}


created_at: 2026-06-08 08:17:00
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: fc50b8a4-9cef-4708-92cd-e212c2069e33
encoding: FLOAT32
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{"ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","page":7,"moddate":"2023-04-05T00:33:07+00:00","title":"","chroma:document":"ChatGPT has shown immense potential in this ﬁeld. Its ability to accurately\nclassify text, ﬂexibility in handling various classiﬁcation tasks, and potential for\ncustomization make it a valuable tool for text classiﬁcation, as evidenced by\nseveral studies in the literature.\n8","subject":"","creationdate":"2023-04-05T00:33:07+00:00","producer":"pdfTeX-1.40.21","keywords":"","creator":"LaTeX with hyperref","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","author":"","page_label":"8","trapped":"/False","total_pages":35}


created_at: 2026-06-08 08:17:00
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: 8363f60a-de45-4053-a714-d52f974580a8
encoding: FLOAT32
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{"moddate":"2023-04-05T00:33:07+00:00","trapped":"/False","page":8,"subject":"","author":"","creationdate":"2023-04-05T00:33:07+00:00","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","chroma:document":"Kuzman et al. [18] employed ChatGPT for automatic genre recognition, with\nthe goal of simplifying the text classiﬁcation task by utilizing ChatGPT’s zero-\nshot classiﬁcation capability. They compared ChatGPT’s genre recognition per-\nformance, using two prompt languages (EN and SL), with the X-GENRE clas-\nsiﬁer based on the multilingual model XLM-RoBERTa on the English dataset\nEN-GINCO and the Slovenian dataset GINCO. The results showed that when","page_label":"9","creator":"LaTeX with hyperref","keywords":"","title":"","producer":"pdfTeX-1.40.21","total_pages":35,"ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2"}


created_at: 2026-06-08 08:17:00
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: 34b081c3-b8ef-4b03-86fb-751d8dcd4b18
encoding: FLOAT32
metadata:
{"trapped":"/False","total_pages":35,"title":"","page_label":"9","keywords":"","moddate":"2023-04-05T00:33:07+00:00","page":8,"author":"","producer":"pdfTeX-1.40.21","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","creationdate":"2023-04-05T00:33:07+00:00","subject":"","chroma:document":"EN was used as the prompt language, ChatGPT achieved Micro F1, Macro F1,\nand Accuracy scores of 0.74, 0.66, and 0.72. However, on the GINCO dataset,\nChatGPT’s genre recognition performance with both EN and SL prompt lan-\nguages was lower than that of the X-GENRE classiﬁer to varying degrees.\nAmin et al. [19] evaluated the text classiﬁcation ability of ChatGPT in\naﬀective computing by using it to perform personality prediction, sentiment","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","creator":"LaTeX with hyperref"}


created_at: 2026-06-08 08:17:00
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: 76585bfc-7cf9-4382-8a06-679e19971b7d
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{"title":"","keywords":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","page":8,"chroma:document":"analysis, and suicide ideation detection tasks. They prompted ChatGPT with\ncorresponding prompts on three datasets: First Impressions, Sentiment140, and\nSuicide and Depression, and compared its classiﬁcation performance with three\nbaseline models: RoBERTa-base, Word2Vec, and BoW. The results showed\nthat ChatGPT’s accuracy and UAR for the ﬁve personality classiﬁcations on\nthe First Impressions dataset were lower than the baseline methods to varying","moddate":"2023-04-05T00:33:07+00:00","subject":"","producer":"pdfTeX-1.40.21","page_label":"9","creator":"LaTeX with hyperref","trapped":"/False","creationdate":"2023-04-05T00:33:07+00:00","total_pages":35,"author":""}


created_at: 2026-06-08 08:17:00
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: cc5228fe-2307-43ff-bf62-ec1f13501795
encoding: FLOAT32
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{"producer":"pdfTeX-1.40.21","title":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","moddate":"2023-04-05T00:33:07+00:00","creator":"LaTeX with hyperref","total_pages":35,"chroma:document":"degrees. On the Sentiment140 dataset, ChatGPT’s accuracy and UAR were 85.5\nand 85.5, respectively, which were better than the three baseline methods. On\nthe Suicide and Depression dataset, ChatGPT’s accuracy and UAR were 92.7\nand 91.2, respectively, which were lower than RoBERTa, the best-performing\nbaseline method.\nZhang et al. [20] employed ChatGPT for stance detection, which includes\nsupport and opposition. They used ChatGPT to classify the political stance","subject":"","keywords":"","trapped":"/False","page_label":"9","page":8,"ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","creationdate":"2023-04-05T00:33:07+00:00","author":""}


created_at: 2026-06-08 08:17:00
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: b0ad354d-c52a-40da-9ae4-7c8935ff7d4a
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{"page_label":"9","page":8,"creationdate":"2023-04-05T00:33:07+00:00","total_pages":35,"chroma:document":"of tweets in the SemEval-2016 and P-Stance datasets. SemEval-2016 contains\n4,870 English tweets, and they selected tweets with the most commonly occur-\nring FM, LA, and HC political labels for stance classiﬁcation. The P-Stance\ndataset has 21,574 English tweets, and they classiﬁed the stance of tweets to-\nwards Trump, Biden, and Bernie. The ﬁnal results showed that on the SemEval-\n2016 dataset, ChatGPT achieved F1-m scores of 68.4, 58.2, and 79.5 for the FM,","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","creator":"LaTeX with hyperref","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","keywords":"","subject":"","author":"","moddate":"2023-04-05T00:33:07+00:00","producer":"pdfTeX-1.40.21","trapped":"/False","title":""}


created_at: 2026-06-08 08:17:00
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: 2cc95063-d535-4ece-b9ea-9ce0cafd0810
encoding: FLOAT32
metadata:
{"ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","creator":"LaTeX with hyperref","page_label":"9","page":8,"moddate":"2023-04-05T00:33:07+00:00","author":"","chroma:document":"LA, and HC political labels, and F1-avg scores of 72.6, 59.3, and 78.0, respec-\ntively. On the P-Stance dataset, ChatGPT achieved F1-m scores of 82.8, 82.3,\nand 79.4 for the Trump, Biden, and Bernie political ﬁgures, and F1-avg scores\nof 83.2, 82.0, and 79.4, respectively.\nHuang et al. [21] used ChatGPT to detect implicit hate speech in tweets.\nThey selected 12.5% (795 tweets) of the LatentHatred dataset containing im-\nplicit hate speech and asked ChatGPT to classify them into three categories:","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","title":"","trapped":"/False","keywords":"","subject":"","total_pages":35,"producer":"pdfTeX-1.40.21","creationdate":"2023-04-05T00:33:07+00:00"}


created_at: 2026-06-08 08:17:00
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: 398f68d6-31d9-4bb2-84b7-108411d6f636
encoding: FLOAT32
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{"total_pages":35,"page_label":"9","creationdate":"2023-04-05T00:33:07+00:00","title":"","subject":"","author":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","keywords":"","trapped":"/False","page":8,"chroma:document":"implicit hate speech, non-hate speech, and uncertain. The results showed that\nChatGPT correctly recognized 636 (80%) of the tweets. The number of tweets\nclassiﬁed as non-hate speech and uncertain were 146 (18.4%) and 13 (1.6%),\nrespectively. The results of the reclassiﬁcation of tweets in the non-hate speech\nand uncertain categories by Amazon Mechanical Turk (Mturk) workers were\nconsistent with ChatGPT’s classiﬁcation.\nOverall, ChatGPT has tremendous potential in text classiﬁcation tasks, as it","producer":"pdfTeX-1.40.21","creator":"LaTeX with hyperref","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","moddate":"2023-04-05T00:33:07+00:00"}


created_at: 2026-06-08 08:17:00
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: 16035aeb-0ef6-4952-8792-32bcdff6f559
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created_at: 2026-06-08 08:17:00
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{"creator":"LaTeX with hyperref","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","title":"","keywords":"","producer":"pdfTeX-1.40.21","creationdate":"2023-04-05T00:33:07+00:00","moddate":"2023-04-05T00:33:07+00:00","subject":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","page_label":"10","total_pages":35,"chroma:document":"stance detection, and more. However, there are still challenges that ChatGPT\nfaces in the ﬁeld of text classiﬁcation. Firstly, it struggles to perform well in\nclassiﬁcation tasks with rare or out-of-vocabulary words since it heavily relies\non the distribution of training data. Additionally, the signiﬁcant computational\nresources required for training and utilizing ChatGPT can limit its use in some\napplications.\n2.1.3 Text generation","page":9,"author":"","trapped":"/False"}


created_at: 2026-06-08 08:17:00
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: a71f21c3-c878-4ba1-8e58-f0d5276cd2ae
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{"moddate":"2023-04-05T00:33:07+00:00","page":9,"trapped":"/False","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","title":"","creator":"LaTeX with hyperref","keywords":"","chroma:document":"applications.\n2.1.3 Text generation\nWe live in an era of information explosion, and text is an eﬃcient way of trans-\nmitting information. The diversity of information has led to a diversity of text\ncategories. When researchers use ChatGPT’s text generation capabilities for\nresearch, they inevitably choose to generate diﬀerent types of text. In the pro-\ncess of reading papers, we found that the word count of the text generated by","producer":"pdfTeX-1.40.21","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","page_label":"10","creationdate":"2023-04-05T00:33:07+00:00","subject":"","author":"","total_pages":35}


created_at: 2026-06-08 08:17:00
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: b1f85c10-d56f-4bf4-855e-9c9a2dcea907
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{"author":"","keywords":"","trapped":"/False","page":9,"total_pages":35,"title":"","chroma:document":"researchers increased from small to large, so we wanted to summarize existing\nresearch based on the size of the text word count. We divided the generated\ntext into three levels: phrases, sentences, and paragraphs.\nThe following article uses ChatGPT to generate phrases. Zhang et al. [22]\nproves that the semantic HAR model with semantic augmentation added dur-\ning training performs better in motion recognition than other models. Semantic","page_label":"10","creationdate":"2023-04-05T00:33:07+00:00","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","subject":"","producer":"pdfTeX-1.40.21","moddate":"2023-04-05T00:33:07+00:00","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","creator":"LaTeX with hyperref"}


created_at: 2026-06-08 08:17:00
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: 4ef65aff-1a4a-4f51-875b-e3f3f477343c
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{"page":9,"subject":"","page_label":"10","creator":"LaTeX with hyperref","title":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","chroma:document":"augmentation requires shared tokens, which is lacking in some datasets. There-\nfore, authors leverage ChatGPT for an automated label generation approach\nfor datasets originally without shared tokens. Fu et al. [23] describes a new\nworkﬂow for converting natural language commands into Bash commands. The\nauthor uses ChatGPT to generate a candidate list of Bash commands based\non user input, and then uses a combination of heuristic and machine learning","author":"","producer":"pdfTeX-1.40.21","keywords":"","total_pages":35,"ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","creationdate":"2023-04-05T00:33:07+00:00","moddate":"2023-04-05T00:33:07+00:00","trapped":"/False"}


created_at: 2026-06-08 08:17:00
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: d8d41df2-85ff-450f-9363-9c15b6e1edad
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{"page_label":"10","subject":"","creator":"LaTeX with hyperref","creationdate":"2023-04-05T00:33:07+00:00","title":"","keywords":"","author":"","page":9,"total_pages":35,"ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","chroma:document":"techniques to rank and select the most likely candidates. This workﬂow was\nevaluated on a real command dataset and achieved high accuracy compared\nto other state-of-the-art methods. Chen et al. [24] used the Bart model and\nChatGPT for the task of summarizing humorous titles and compared the perfor-\nmance of the two models. It was found that the Bart model performed better on\nlarge datasets, but ChatGPT was competitive with our best ﬁne-tuned model\nin a small range (48), albeit slightly weaker.","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","moddate":"2023-04-05T00:33:07+00:00","trapped":"/False","producer":"pdfTeX-1.40.21"}


created_at: 2026-06-08 08:17:00
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: a4ea63ce-9434-4989-83fd-1708e0d2d3c9
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{"page_label":"10","keywords":"","total_pages":35,"creationdate":"2023-04-05T00:33:07+00:00","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","creator":"LaTeX with hyperref","moddate":"2023-04-05T00:33:07+00:00","page":9,"subject":"","author":"","trapped":"/False","title":"","chroma:document":"in a small range (48), albeit slightly weaker.\nThe following article uses ChatGPT to generate sentences.Chen et al. [25]\nconstructed a dialogue dataset (HPD) with scenes, timelines, character at-\ntributes, and character relationships in order to use ChatGPT as a conver-\nsational agent to generate dialogue. However, ChatGPT’s performance on the\ntest set was poor, and there is room for improvement.In study [26], chatGPT\ndemonstrated its ability to simplify complex text by providing three ﬁctional","producer":"pdfTeX-1.40.21"}


created_at: 2026-06-08 08:17:00
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: 6fc0e604-b7dd-4813-89f6-2b21699bf644
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{"trapped":"/False","subject":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","chroma:document":"radiology reports to chatGPT for simpliﬁcation. Most radiologists found the\nsimpliﬁed reports to be accurate and complete, with no potential harm to pa-\ntients. However, some errors, omissions of critical medical information and text\npassages were identiﬁed, which could potentially lead to harmful conclusions if\nnot understood by the physicians. Xia et al. [27] proposes a new program re-\npair paradigm called Session-based Automatic Program Repair (APR). In APR,","page_label":"10","creationdate":"2023-04-05T00:33:07+00:00","author":"","producer":"pdfTeX-1.40.21","title":"","moddate":"2023-04-05T00:33:07+00:00","total_pages":35,"page":9,"creator":"LaTeX with hyperref","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","keywords":""}


created_at: 2026-06-08 08:17:00
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: 59bbac1c-b847-4fdb-96fb-a748d73b1815
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{"creator":"LaTeX with hyperref","author":"","subject":"","title":"","page_label":"10","page":9,"total_pages":35,"keywords":"","trapped":"/False","chroma:document":"the previously generated patches are iteratively built upon by combining them\n10","producer":"pdfTeX-1.40.21","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","creationdate":"2023-04-05T00:33:07+00:00","moddate":"2023-04-05T00:33:07+00:00","source":"./data/Summary of ChatGPTGPT-4 Research.pdf"}


created_at: 2026-06-08 08:17:00
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: db80434d-448a-49b7-a8fd-47d4200e07a7
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{"author":"","creationdate":"2023-04-05T00:33:07+00:00","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","chroma:document":"with validation feedback to construct the model’s input. The eﬀectiveness of\nthe approach is veriﬁed using the QuixBugs dataset. The experiment shows\nthat ChatGPT ﬁne-tuned with reinforcement learning from human feedback\n(RLHF) outperforms Codex trained unsupervisedly in both repair datasets. In\nreference to study [28], ChatGPT was compared to three commercial transla-\ntion products: Google Translate2, DeepL Translate3, and Tencent TranSmart4.","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","trapped":"/False","title":"","creator":"LaTeX with hyperref","page_label":"11","moddate":"2023-04-05T00:33:07+00:00","keywords":"","total_pages":35,"producer":"pdfTeX-1.40.21","page":10,"subject":""}


created_at: 2026-06-08 08:17:00
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: c560e2f5-4c91-4359-9188-323367c60f29
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{"keywords":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","author":"","subject":"","chroma:document":"The evaluation was conducted on the Flores101 test set, using the WMT19\nbiomedical translation task to test translation robustness, with BLEU score as\nthe main metric. The study found that ChatGPT is competitive with commer-\ncial translation products on high-resource European languages but falls behind\non low-resource or distant languages. The authors explored an interesting strat-\negy called pivot prompts, which signiﬁcantly improved translation performance.","trapped":"/False","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","moddate":"2023-04-05T00:33:07+00:00","total_pages":35,"creationdate":"2023-04-05T00:33:07+00:00","producer":"pdfTeX-1.40.21","page_label":"11","page":10,"creator":"LaTeX with hyperref","title":""}


created_at: 2026-06-08 08:17:00
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: b3149378-4189-4edd-8974-2c20edaae3cd
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{"title":"","moddate":"2023-04-05T00:33:07+00:00","subject":"","total_pages":35,"page":10,"producer":"pdfTeX-1.40.21","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","keywords":"","trapped":"/False","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","chroma:document":"While ChatGPT did not perform as well as commercial systems on biomedical\nabstracts or Reddit comments, it may be a good speech translator. Prieto et\nal. [29] evaluated the use of ChatGPT in developing an automated construction\nschedule based on natural language prompts. The experiment required building\nnew partitions in an existing space and providing details on the rooms to be\npartitioned. The results showed that ChatGPT was able to generate a coher-","page_label":"11","creator":"LaTeX with hyperref","author":"","creationdate":"2023-04-05T00:33:07+00:00"}


created_at: 2026-06-08 08:17:00
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: d4abd733-1fff-4ccd-b596-2ca531a1bd1b
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{"creationdate":"2023-04-05T00:33:07+00:00","total_pages":35,"creator":"LaTeX with hyperref","keywords":"","page_label":"11","moddate":"2023-04-05T00:33:07+00:00","title":"","chroma:document":"ent schedule that followed a logical approach to meet the requirements of the\ngiven scope. However, there were still several major ﬂaws that would limit the\nuse of this tool in real-world projects.Michail et al. [30] proposed a method\nto improve the prediction accuracy of the HeFit ﬁne-tuned XLM T model on\ntweet intimacy by generating a dataset of tweets with intimacy rating tags using\nChatGPT. The speciﬁc operation is to input tweets with intimacy rating tags","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","trapped":"/False","author":"","page":10,"ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","subject":"","producer":"pdfTeX-1.40.21"}


created_at: 2026-06-08 08:17:00
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: 8399add2-ba02-4ad6-b85d-b5ce05760463
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{"creationdate":"2023-04-05T00:33:07+00:00","author":"","total_pages":35,"trapped":"/False","chroma:document":"into ChatGPT and then output similar tweets.\nThe following article uses ChatGPT to generate paragraphs. Wang et al.\n[31] compared the abstract summarization performance of ChatGPT and other\nmodels on various cross-lingual text datasets and found that ChatGPT may\nperform worse in metrics such as R 1, R 2, R L, and B S. Yang et al. [32]\nsummarized the performance of ChatGPT in question answering-based text\nsummarization and found that, compared to ﬁne-tuned models, ChatGPT’s","page_label":"11","keywords":"","moddate":"2023-04-05T00:33:07+00:00","title":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","page":10,"creator":"LaTeX with hyperref","producer":"pdfTeX-1.40.21","subject":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf"}


created_at: 2026-06-08 08:17:00
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: ef4908ef-32ab-4d8e-9118-b396c609a13f
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{"ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","title":"","chroma:document":"performance is slightly worse in all performance metrics. However, the article\nsuggests that if the dataset is golden annotation, ChatGPT’s performance may\nsurpass ﬁne-tuned models in these metrics. Belouadi et al. [33] compared the\nability of ByGPT5 and ChatGPT trained on a range of labeled and unlabeled\ndatasets of English and German poetry to generate constrained style poetry, and\nevaluated them using three metrics: Rhyme, ScoreAlliteration, and ScoreMe-","total_pages":35,"source":"./data/Summary of ChatGPTGPT-4 Research.pdf","author":"","page_label":"11","creationdate":"2023-04-05T00:33:07+00:00","trapped":"/False","producer":"pdfTeX-1.40.21","creator":"LaTeX with hyperref","page":10,"subject":"","moddate":"2023-04-05T00:33:07+00:00","keywords":""}


created_at: 2026-06-08 08:17:00
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: dbee0bbe-379c-4d23-928a-867c65463912
encoding: FLOAT32
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{"author":"","creator":"LaTeX with hyperref","moddate":"2023-04-05T00:33:07+00:00","keywords":"","creationdate":"2023-04-05T00:33:07+00:00","page":10,"subject":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","trapped":"/False","chroma:document":"ter Score. The conclusion is that ByGPT5 performs better than ChatGPT.\nBlanco-Gonzalez et al. [34] evaluated chatGPT’s ability to write commentary\narticles, and in fact, this article itself was written by chatGPT. The human au-\nthor rewrote the manuscript based on chatGPT’s draft. Experts found that it\ncan quickly generate and optimize text, as well as help users complete multiple\ntasks. However, in terms of generating new content, it is not ideal. Ultimately,","producer":"pdfTeX-1.40.21","total_pages":35,"source":"./data/Summary of ChatGPTGPT-4 Research.pdf","page_label":"11","title":""}


created_at: 2026-06-08 08:17:00
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: 6f3c7433-3398-4846-aee7-fac2989407b8
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{"creator":"LaTeX with hyperref","chroma:document":"it can be said that without strong human intervention, chatGPT is not a use-\nful tool for writing reliable scientiﬁc texts. It lacks the knowledge and expertise\nrequired to accurately and fully convey complex scientiﬁc concepts and informa-\n11","creationdate":"2023-04-05T00:33:07+00:00","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","page":10,"trapped":"/False","producer":"pdfTeX-1.40.21","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","total_pages":35,"subject":"","moddate":"2023-04-05T00:33:07+00:00","page_label":"11","title":"","author":"","keywords":""}


created_at: 2026-06-08 08:17:00
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: 736ee097-e907-4846-abd3-b7a78f779f09
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{"title":"","keywords":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","trapped":"/False","creationdate":"2023-04-05T00:33:07+00:00","total_pages":35,"page_label":"12","moddate":"2023-04-05T00:33:07+00:00","page":11,"subject":"","producer":"pdfTeX-1.40.21","chroma:document":"tion. Khalil et al. [35] on the originality of content generated by ChatGPT. To\nevaluate the originality of 50 papers on various topics generated by ChatGPT,\ntwo popular plagiarism detection tools, Turnitin and iThenticate, were used.\nThe results showed that ChatGPT has great potential in generating complex\ntext output that is not easily captured by plagiarism detection software. The\nexisting plagiarism detection software should update their plagiarism detection","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","creator":"LaTeX with hyperref","author":""}


created_at: 2026-06-08 08:17:00
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: f0eeee58-ba9d-4ca9-bc15-b4a693e4e46b
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{"author":"","producer":"pdfTeX-1.40.21","trapped":"/False","keywords":"","creator":"LaTeX with hyperref","page":11,"source":"./data/Summary of ChatGPTGPT-4 Research.pdf","page_label":"12","moddate":"2023-04-05T00:33:07+00:00","subject":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","chroma:document":"engines. Basic et al. [36] conducted a comparison of the writing performance\nof students using or not using ChatGPT-3 as a writing aid. The experiment\nconsisted of two groups of 9 participants each. The control group wrote articles\nusing traditional methods, while the experimental group used ChatGPT as an\naid. Two teachers evaluated the papers. The study showed that the assistance of\nChatGPT did not necessarily improve the quality of the students’ essays.Noever","total_pages":35,"creationdate":"2023-04-05T00:33:07+00:00","title":""}


created_at: 2026-06-08 08:17:00
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: 4dfb4869-2e9e-4d5e-9967-73e0df1d98e2
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created_at: 2026-06-08 08:17:00
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: 93da8720-4071-412b-b27a-a1b2fba43f5e
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{"keywords":"","moddate":"2023-04-05T00:33:07+00:00","page_label":"12","subject":"","trapped":"/False","total_pages":35,"ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","producer":"pdfTeX-1.40.21","author":"","creationdate":"2023-04-05T00:33:07+00:00","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","title":"","creator":"LaTeX with hyperref","chroma:document":"unable to distinguish between the chatbot and a real human, highlighting the\npotential for these AI chatbots to be used for deceptive purposes.\n2.1.4 Code Generation\nCode generation refers to the process of automatically generating computer code\nfrom high-level descriptions or speciﬁcations. ChatGPT’s advanced natural lan-\nguage processing capabilities make it capable of performing code generation\ntasks. By analyzing the requirements for code generation, ChatGPT can pro-","page":11}


created_at: 2026-06-08 08:17:00
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: acb2b4e5-4131-4e94-8909-e27f5a4de4fa
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{"keywords":"","creator":"LaTeX with hyperref","title":"","page":11,"author":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","trapped":"/False","chroma:document":"duce code snippets that accurately execute the intended functionality. This not\nonly saves time and eﬀort in writing code from scratch but also reduces the risk\nof errors that may occur during manual coding. In addition, ChatGPT’s ability\nto learn and adapt to new programming languages and frameworks enables it\nto complete more complex programming tasks. For example:\nMegahed et al. [38] discussed the potential of using ChatGPT for tasks","total_pages":35,"source":"./data/Summary of ChatGPTGPT-4 Research.pdf","creationdate":"2023-04-05T00:33:07+00:00","moddate":"2023-04-05T00:33:07+00:00","producer":"pdfTeX-1.40.21","subject":"","page_label":"12"}


created_at: 2026-06-08 08:17:00
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: b0c81f20-4c03-4d64-af30-6940d1758202
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{"page":11,"source":"./data/Summary of ChatGPTGPT-4 Research.pdf","page_label":"12","author":"","producer":"pdfTeX-1.40.21","moddate":"2023-04-05T00:33:07+00:00","subject":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","creator":"LaTeX with hyperref","creationdate":"2023-04-05T00:33:07+00:00","chroma:document":"such as code explanation, suggesting alternative methods for problem-solving\nwith code, and translating code between programming languages. The solutions\nprovided by ChatGPT were found to be viable. In another study, Treude et\nal. [39] introduced a ChatGPT-based prototype called GPTCOMCARE, which\nhelps programmers generate multiple solutions for a programming problem and\nhighlight the diﬀerences between each solution using colors.Sobania et al. [40]","title":"","keywords":"","trapped":"/False","total_pages":35}


created_at: 2026-06-08 08:17:00
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{"subject":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","trapped":"/False","keywords":"","creator":"LaTeX with hyperref","producer":"pdfTeX-1.40.21","total_pages":35,"chroma:document":"utilized ChatGPT for code bug ﬁxing, and further improved the success rate of\nbug ﬁxing by inputting more information through its dialogue system. Speciﬁ-\ncally, the QuixBugs standard bug ﬁxing benchmark contained 40 code bugs that\nneeded to be ﬁxed. With limited information, ChatGPT ﬁxed 19 bugs, which\nwas slightly lower than the 21 bugs ﬁxed by the Codex model, but signiﬁcantly\nhigher than the 7 ﬁxed by the Standard APR model. When given more prompts","page":11,"title":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","moddate":"2023-04-05T00:33:07+00:00","author":"","creationdate":"2023-04-05T00:33:07+00:00","page_label":"12"}


created_at: 2026-06-08 08:17:00
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
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created_at: 2026-06-08 08:17:00
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: 664fb4fe-4a87-4d27-9836-713713a40f30
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{"producer":"pdfTeX-1.40.21","trapped":"/False","chroma:document":"for code bug ﬁxing tasks.Xia et al. [27] proposed a conversational approach\nfor Automated Program Repair (APR), which alternates between generating\npatches and validating them against feedback from test cases until the correct\npatch is generated. Selecting 30 bugs from the QuixBugs standard bug ﬁxing\nbenchmark, which are suitable for test case feedback, and demonstrating them\nwith Java and Python, the QuixBugs-Python and QuixBugs-Java datasets were","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","creationdate":"2023-04-05T00:33:07+00:00","page":12,"moddate":"2023-04-05T00:33:07+00:00","keywords":"","subject":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","title":"","page_label":"13","creator":"LaTeX with hyperref","author":"","total_pages":35}


created_at: 2026-06-08 08:17:00
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: 573a7f0d-3fb5-4c0f-9707-10eec553108a
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created_at: 2026-06-08 08:17:00
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
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created_at: 2026-06-08 08:17:00
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: c73691b7-944f-4da3-b41a-2e4a52a25f2f
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{"trapped":"/False","creationdate":"2023-04-05T00:33:07+00:00","page_label":"13","page":12,"ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","subject":"","chroma:document":"ChatGPT can access structured datasets and perform basic software opera-\ntions required by databases, such as create, read, update, and delete (CRUD).\nThis suggests that cutting-edge language models like ChatGPT have the nec-\nessary scale to tackle complex problems. McKee et al. [42] utilized ChatGPT\nas an experimental platform to investigate cybersecurity issues. They modeled\nﬁve diﬀerent modes of computer virus properties, including self-replication, self-","moddate":"2023-04-05T00:33:07+00:00","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","keywords":"","total_pages":35,"creator":"LaTeX with hyperref","title":"","author":"","producer":"pdfTeX-1.40.21"}


created_at: 2026-06-08 08:17:00
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
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{"page":12,"source":"./data/Summary of ChatGPTGPT-4 Research.pdf","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","trapped":"/False","total_pages":35,"keywords":"","page_label":"13","title":"","producer":"pdfTeX-1.40.21","creator":"LaTeX with hyperref","creationdate":"2023-04-05T00:33:07+00:00","author":"","subject":"","moddate":"2023-04-05T00:33:07+00:00","chroma:document":"modiﬁcation, execution, evasion, and application, using ChatGPT. These ﬁve\nmodes encompassed thirteen encoding tasks from credential access to defense\nevasion within the MITRE ATT&CK framework. The results showed that the\nquality of ChatGPT’s generated code was generally above average, except for the\nself-replication mode, where it performed poorly.They [43] also employed Chat-\nGPT as a network honeypot to defend against attackers. By having ChatGPT"}


created_at: 2026-06-08 08:17:00
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
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created_at: 2026-06-08 08:17:00
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
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created_at: 2026-06-08 08:17:00
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
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created_at: 2026-06-08 08:17:00
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
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created_at: 2026-06-08 08:17:00
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
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{"keywords":"","page_label":"14","creator":"LaTeX with hyperref","creationdate":"2023-04-05T00:33:07+00:00","title":"","chroma:document":"methods to arrive at a conclusion. Inference is an important ability in human\nthinking, and is often used to solve problems, make decisions, analyze and eval-\nuate information, etc. Inference also plays a key role in ﬁelds such as science,\nphilosophy, law, etc. There are two types of inference: inductive reasoning,\nwhich involves deriving general rules or conclusions from known facts or expe-\nriences, and deductive reasoning, which involves deriving speciﬁc conclusions","page":13,"moddate":"2023-04-05T00:33:07+00:00","author":"","total_pages":35,"subject":"","trapped":"/False","producer":"pdfTeX-1.40.21","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","source":"./data/Summary of ChatGPTGPT-4 Research.pdf"}


created_at: 2026-06-08 08:17:00
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: 4c37c20c-5ac7-43af-8561-f8d345c46b08
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created_at: 2026-06-08 08:17:00
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
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created_at: 2026-06-08 08:17:00
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
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{"keywords":"","moddate":"2023-04-05T00:33:07+00:00","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","total_pages":35,"page_label":"14","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","chroma:document":"the presence of bias in the discourse surrounding chronic manifestations of the\ndisease using the SHAP tool. The author also envisioned ChatGPT as a replace-\nment for the BERT model for scoring the emotional value of text. Huang et al.\n[21] chose 12.5% of individuals in the potential hate dataset as study materials,\ninduced ChatGPT to make classiﬁcations based on a prompt, and ChatGPT\nproduced three classiﬁcations: unclear, yes, and no. The author assigned a","subject":"","title":"","creator":"LaTeX with hyperref","author":"","trapped":"/False","producer":"pdfTeX-1.40.21","page":13,"creationdate":"2023-04-05T00:33:07+00:00"}


created_at: 2026-06-08 08:17:00
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: 418d8603-c39f-4da6-8052-296216fc50a2
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created_at: 2026-06-08 08:17:00
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: b90173bb-bc81-41f1-a62e-5fa6d77805c4
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created_at: 2026-06-08 08:17:00
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
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{"page_label":"15","chroma:document":"tainty in the decision-making process: in some cases, large language models\ncan arrive at the correct answer through incorrect reasoning; and it may make\nsuboptimal decisions for simple reasoning problems. Ortega-Martn et al. [46]\nhad ChatGPT detect three diﬀerent levels of language ambiguity and evaluated\nits performance. The conclusion is that In semantics, ChatGPT performed per-\nfectly in the detection of ambiguities. Apart from that, it has some bright sports","creator":"LaTeX with hyperref","author":"","title":"","trapped":"/False","total_pages":35,"page":14,"creationdate":"2023-04-05T00:33:07+00:00","producer":"pdfTeX-1.40.21","subject":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","moddate":"2023-04-05T00:33:07+00:00","keywords":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2"}


created_at: 2026-06-08 08:17:00
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: 96e5c9a6-555a-4d0c-b206-e00f37c0355b
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created_at: 2026-06-08 08:17:00
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: 944f0c18-3aba-4184-bf84-d2d24f2f5ad0
encoding: FLOAT32
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{"creationdate":"2023-04-05T00:33:07+00:00","page":14,"page_label":"15","title":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","author":"","trapped":"/False","producer":"pdfTeX-1.40.21","creator":"LaTeX with hyperref","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","total_pages":35,"keywords":"","subject":"","chroma:document":"processing\nData Visualization\nNatural language interfaces have contributed to generating visualizations\ndirectly from natural language, but visualization problems remain challenging\ndue to the ambiguity of natural language.ChatGPT provides a new avenue for\nthe ﬁeld by converting natural language into visualized code.\nIn terms of data visualization, Noever et al. [41] tested ChatGPT’s ba-\nsic arithmetic skills by asking questions.On the iris dataset, Titanic survival","moddate":"2023-04-05T00:33:07+00:00"}


created_at: 2026-06-08 08:17:00
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: fafa2584-58e5-4096-a8d2-09ec65ec289a
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{"title":"","page":14,"chroma:document":"dataset, Boston housing data, and randomly generated insurance claims dataset,\nthe statistical analysis of data and visualization problems were converted to\nprogramming problems using Jupyter to verify ChatGPT’s ability to generate\npython code to draw suitable graphs and analyze the data. The results show\nthat ChatGPT can access structured and organized datasets to perform the\nfour basic software operations required for databases: create, read, update, and","creationdate":"2023-04-05T00:33:07+00:00","total_pages":35,"author":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","trapped":"/False","keywords":"","producer":"pdfTeX-1.40.21","creator":"LaTeX with hyperref","subject":"","moddate":"2023-04-05T00:33:07+00:00","page_label":"15"}


created_at: 2026-06-08 08:17:00
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: b7853dc6-462d-4d57-939d-52f04fca1796
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created_at: 2026-06-08 08:17:00
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{"total_pages":35,"ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","producer":"pdfTeX-1.40.21","trapped":"/False","chroma:document":"capabilities to select the appropriate visualization type to generate the code for\nvisualization. In this paper,the reseachers compare the visualization results of\nGPT-3, Codex and ChatGPT in the case of nvBench SQLite database [48] and\nthe visualization results of energy production dataset in the study of ADVISor\nwith NL4DV [49, 50].In addition to, they explore the ability to reason and hy-\npothesize of the LLM on movie dataset [48] when the hints are insuﬃcient or","author":"","page_label":"15","page":14,"keywords":"","subject":"","creationdate":"2023-04-05T00:33:07+00:00","moddate":"2023-04-05T00:33:07+00:00","creator":"LaTeX with hyperref","title":""}


created_at: 2026-06-08 08:17:00
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
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{"total_pages":35,"creationdate":"2023-04-05T00:33:07+00:00","moddate":"2023-04-05T00:33:07+00:00","title":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","subject":"","author":"","keywords":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","trapped":"/False","producer":"pdfTeX-1.40.21","chroma:document":"wrong .Experimental results show that LLM can eﬀectively support the end-to-\nend generation of visualization results from natural language when supported\nby hints, providing an eﬃcient, reliable and accurate solution to the natural\nlanguage visualization problem.\n15","page_label":"15","creator":"LaTeX with hyperref","page":14}


created_at: 2026-06-08 08:17:00
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: 8efafb5d-f097-4b4a-8324-3504e7b4b9ab
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{"subject":"","title":"","chroma:document":"Information Extraction\nThe goal of information extraction is to extract speciﬁc information from\nnatural language text for structured representation, including three important\nsubtasks such as entity relationship extraction, named entity recognition, and\nevent extraction, which have wide applications in business, medical, and other\nﬁelds.\nIn information extraction, Wei et al. [51] proposed ChatIE, a ChatGPT-\nbased multi-round question-and-answer framework for information extraction.","total_pages":35,"page_label":"16","producer":"pdfTeX-1.40.21","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","trapped":"/False","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","page":15,"keywords":"","creationdate":"2023-04-05T00:33:07+00:00","author":"","moddate":"2023-04-05T00:33:07+00:00","creator":"LaTeX with hyperref"}


created_at: 2026-06-08 08:17:00
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: 4f1569fc-86f5-407a-834c-50e06bc58abf
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{"chroma:document":"The framework decomposes a complex information extraction (IE) task into\nseveral parts, then combines the results of each round into a ﬁnal structured\nresult. The entity association triple extraction, named entity recognition, and\nevent extraction tasks were performed on six datasets NYT11-HRL, DuIE2.0\n, conllpp, MSR , DuEE1.0 [52, 53, 54, 55, 56], and ACE05 in both languages,\ncomparing three metrics of precision, recall, and F1 score.These results sug-","subject":"","page_label":"16","author":"","producer":"pdfTeX-1.40.21","creator":"LaTeX with hyperref","creationdate":"2023-04-05T00:33:07+00:00","trapped":"/False","total_pages":35,"keywords":"","moddate":"2023-04-05T00:33:07+00:00","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","title":"","page":15,"source":"./data/Summary of ChatGPTGPT-4 Research.pdf"}


created_at: 2026-06-08 08:17:00
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: cf7ab688-9069-4a29-a37d-59fbfead3c13
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{"source":"./data/Summary of ChatGPTGPT-4 Research.pdf","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","creator":"LaTeX with hyperref","moddate":"2023-04-05T00:33:07+00:00","chroma:document":"gest that on six widely used IE datasets, ChatIE improves performance by an\naverage of 18.98% compared to the original ChatGPT without ChatIE, and out-\nperforms the supervised models FCM and MultiR [57, 58] on the NYT11-HRL\ndataset.While the original ChatGPT cannot solve complex IE problems with\noriginal task instructions, and with this framework, successfully IE tasks were\nimplemented on six datasets.Gao et al. [59] explored the feasibility and chal-","producer":"pdfTeX-1.40.21","keywords":"","creationdate":"2023-04-05T00:33:07+00:00","subject":"","title":"","page_label":"16","page":15,"total_pages":35,"author":"","trapped":"/False"}


created_at: 2026-06-08 08:17:00
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
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created_at: 2026-06-08 08:17:00
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: 29de7e00-17c6-465f-9e70-7c5485eec2fe
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{"page_label":"16","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","creationdate":"2023-04-05T00:33:07+00:00","author":"","producer":"pdfTeX-1.40.21","chroma:document":"models such as EEQA. Continuous reﬁnement of cues does not lead to consis-\ntent performance improvements, and ChatGPT is highly sensitive to diﬀerent\ncue styles.Tang et al. [62] proposed a new training paradigm that incorporates\nappropriate cues to guide ChatGPT to generate a variety of examples with dif-\nferent sentence structures and language patterns and eliminate the resulting\nlow-quality or duplicate samples for downstream tasks. Although compared to","trapped":"/False","page":15,"source":"./data/Summary of ChatGPTGPT-4 Research.pdf","title":"","subject":"","moddate":"2023-04-05T00:33:07+00:00","keywords":"","total_pages":35,"creator":"LaTeX with hyperref"}


created_at: 2026-06-08 08:17:00
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
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{"page_label":"16","keywords":"","trapped":"/False","title":"","creationdate":"2023-04-05T00:33:07+00:00","producer":"pdfTeX-1.40.21","creator":"LaTeX with hyperref","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","subject":"","chroma:document":"a soft model for a speciﬁc healthcare tasks, ChatGPT underperforms in Named\nEntity Recognition (NER) and Relationship Extraction (RE) tasks , in the Gene\nAssociation Database (GAD) Release; EU-ADR corpus for the RE task , the\ninnovative training framework was able to train local models, with F1 scores im-\nproving from 23.37% to 63.99% for the named entity recognition task and from\n75%, while alleviating privacy concerns and time-consuming data collection and","moddate":"2023-04-05T00:33:07+00:00","page":15,"ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","author":"","total_pages":35}


created_at: 2026-06-08 08:17:00
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: 5a1a095f-037e-4b7b-bb18-aef7fec9a714
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{"author":"","page":15,"page_label":"16","trapped":"/False","keywords":"","creationdate":"2023-04-05T00:33:07+00:00","moddate":"2023-04-05T00:33:07+00:00","creator":"LaTeX with hyperref","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","total_pages":35,"chroma:document":"annotation problems.He et al. [63] proposed a contextual learning framework\nICL- D3IE. this framework introduces formatted presentation, continuously it-\nerates to update and improve the presentation, and then combines ChatGPT\nfor text information extraction. In the paper, ICL-D3IE is compared with ex-\nisting pre-trained models such as LiLT,BROS (in-distribution (ID) setting and\nout-of-distribution (OOD) setting) on datasets (FUNSD, CORD, and SROIE","producer":"pdfTeX-1.40.21","title":"","subject":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf"}


created_at: 2026-06-08 08:17:00
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
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{"creationdate":"2023-04-05T00:33:07+00:00","chroma:document":"[64, 65, 66]).These results show that the ICL-D3IE method in all datasets and\nsettings except for the ID setting on CORD are superior to other methods,\n16","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","trapped":"/False","subject":"","author":"","total_pages":35,"producer":"pdfTeX-1.40.21","moddate":"2023-04-05T00:33:07+00:00","title":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","page":15,"page_label":"16","creator":"LaTeX with hyperref","keywords":""}


created_at: 2026-06-08 08:17:00
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: 026f14d3-166a-4f20-bb10-fe9962579670
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{"page":16,"author":"","chroma:document":"with ICL-D3IE (GPT-3) F1 scores reaching 90.32% on FUNSD and97.88% on\nSROIE; in the out-of-distribution (OOD) setting, ICL-D3IE performs much\nbetter than previous pre-trained methods on all datasets.Polak et al. [67] pro-\nposed ChatExtract method - consisting of a set of engineering prompts applied\nto a conversational LLM - for automatic data extraction. In the experiment,\nthey extracted a large number of sentences from hundreds of papers and ran-","producer":"pdfTeX-1.40.21","creationdate":"2023-04-05T00:33:07+00:00","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","page_label":"17","title":"","keywords":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","subject":"","moddate":"2023-04-05T00:33:07+00:00","creator":"LaTeX with hyperref","total_pages":35,"trapped":"/False"}


created_at: 2026-06-08 08:17:00
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
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{"trapped":"/False","chroma:document":"domly selected 100 sentences containing data and 100 sentences without data\nas test data. The results show that the accuracy and recall of LLM exceeded\n90% and may be comparable to human accuracy in many cases; in addition to\nthis, the experiments were conducted under the condition of removing follow-up\nprompts and not keeping the conversation compared to previous experiments,\nrespectively. The accuracy of deleting follow-up questions dropped to 80.2%","creator":"LaTeX with hyperref","page_label":"17","title":"","subject":"","keywords":"","page":16,"author":"","total_pages":35,"ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","creationdate":"2023-04-05T00:33:07+00:00","moddate":"2023-04-05T00:33:07+00:00","producer":"pdfTeX-1.40.21","source":"./data/Summary of ChatGPTGPT-4 Research.pdf"}


created_at: 2026-06-08 08:17:00
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: 5484b373-d334-4e5d-a6f9-9877c6a155cb
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{"author":"","page":16,"total_pages":35,"chroma:document":"and the recall rate dropped to 88.0%. Removing the conversational aspect and\nrelated information retention recall and accuracy dropped to 90.0% and 56.6%,\nrespectively, demonstrating the eﬀect of information retention combined with\npurposeful redundancy on LLM information extraction performance.\nQuality Assessment\nFor translation quality, text generation quality, manual assessment is usually\neﬀective but suﬀers from subjectivity and time-consuming, etc. It was found","trapped":"/False","title":"","moddate":"2023-04-05T00:33:07+00:00","page_label":"17","producer":"pdfTeX-1.40.21","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","keywords":"","subject":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","creator":"LaTeX with hyperref","creationdate":"2023-04-05T00:33:07+00:00"}


created_at: 2026-06-08 08:17:00
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: 3bd98d75-4a11-45eb-a310-008c2f8fc1c9
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{"subject":"","total_pages":35,"source":"./data/Summary of ChatGPTGPT-4 Research.pdf","keywords":"","page":16,"moddate":"2023-04-05T00:33:07+00:00","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","trapped":"/False","creationdate":"2023-04-05T00:33:07+00:00","creator":"LaTeX with hyperref","page_label":"17","chroma:document":"through exploration that ChatGPT has also achieved signiﬁcant performance\nin automatic quality assessment.\nIn terms of quality assessment,Kocmi et al. [68] proposed a GPT-based\ntranslation quality assessment metric, GEMBA, which evaluates the transla-\ntion of each fragment individually and then averages all the obtained scores to\nobtain a ﬁnal system-level score. In the MQM2022 test set (English-German,\nEnglish-Russian, and Chinese-English) [69], a scoring task was performed with","author":"","producer":"pdfTeX-1.40.21","title":""}


created_at: 2026-06-08 08:17:00
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: 68d16972-2d4d-4df6-a7b6-5a3a5e5c37d4
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{"creator":"LaTeX with hyperref","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","chroma:document":"a classiﬁcation task to compare the accuracy [70] and kendall tau scores [71] of\nseven GPT models under four cue templates.The results showed that GEMBA\nhad the highest system-level accuracy of 88.0% compared to more than 10 au-\ntomatic metrics such as BLEU, and among the seven GPT models, ChatGPT\naccuracy is above 80%, in addition to, the best performance can be obtained in\nthe least constrained template, demonstrating the potential of LLM for trans-","moddate":"2023-04-05T00:33:07+00:00","page_label":"17","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","keywords":"","creationdate":"2023-04-05T00:33:07+00:00","producer":"pdfTeX-1.40.21","page":16,"total_pages":35,"trapped":"/False","title":"","author":"","subject":""}


created_at: 2026-06-08 08:17:00
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: 6bed95ec-9f52-4ea0-a944-981a0160a237
encoding: FLOAT32
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{"title":"","page":16,"ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","trapped":"/False","moddate":"2023-04-05T00:33:07+00:00","chroma:document":"lation quality assessment tasks, but the evaluation is only applicable at the\nsystem level and needs further improvement.Wang et al. [72] used ChatGPT\nas a natural language generation (NLG) evaluator to study the correlation with\nhuman judgment. On three datasets covering diﬀerent NLG tasks, task- and\naspect-speciﬁc cues were designed to guide ChatGPT for NLG evaluation in\nCNN/DM [73], OpenMEVA-ROC, and BAGEL for summary, story generation,","keywords":"","producer":"pdfTeX-1.40.21","author":"","subject":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","page_label":"17","creator":"LaTeX with hyperref","creationdate":"2023-04-05T00:33:07+00:00","total_pages":35}


created_at: 2026-06-08 08:17:00
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: 77f102ae-154c-4411-b550-2408dd0cb3f0
encoding: FLOAT32
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{"trapped":"/False","page_label":"17","producer":"pdfTeX-1.40.21","author":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","subject":"","total_pages":35,"page":16,"creationdate":"2023-04-05T00:33:07+00:00","title":"","moddate":"2023-04-05T00:33:07+00:00","creator":"LaTeX with hyperref","keywords":"","chroma:document":"and data-to-text scoring, respectively. Then,they compute Spearman coeﬃ-\ncients [74],Pearson correlation coeﬃcients [75]. Kendall’s Tau score [76] to as-\nsess the correlation with human evaluations.The results show that ChatGPT is\nhighly correlated with human judgments in all aspects, with correlation coeﬃ-\ncients of 0.4 or more in all categories, showing its potential as an NLG indicator.\n17","source":"./data/Summary of ChatGPTGPT-4 Research.pdf"}


created_at: 2026-06-08 08:17:00
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: e7de247f-a8e2-47e0-8bc3-132911b516c3
encoding: FLOAT32
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{"subject":"","chroma:document":"Data Augmentation\nIn natural language processing, text data augmentation is an eﬀective mea-\nsure to alleviate the problem of low data quantity and low quality training data,\nand ChatGPT has shown great potential in this regard.\nIn terms of data augmentation, Dai et al. [77] proposed a ChatGPT-based\ntext data augmentation method that reformulates each sentence in the train-\ning sample into multiple conceptually similar but semantically diﬀerent samples","keywords":"","total_pages":35,"source":"./data/Summary of ChatGPTGPT-4 Research.pdf","author":"","moddate":"2023-04-05T00:33:07+00:00","trapped":"/False","title":"","producer":"pdfTeX-1.40.21","creationdate":"2023-04-05T00:33:07+00:00","page_label":"18","page":17,"ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","creator":"LaTeX with hyperref"}


created_at: 2026-06-08 08:17:00
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: e5edc97d-4582-4d03-96ac-480000a3b927
encoding: FLOAT32
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{"chroma:document":"for classiﬁcation tasks downstream of the Bert model.On text transcriptions\nand PubMed 20k datasets containing more than 8 hours of audio data of com-\nmon medical symptom descriptions,experiments were conducted to compare co-\nsine similarity and TransRate metrics with multiple data enhancement methods\n[9].This paper shows that compared with existing data enhancement methods,\nthe proposed ChatAug method shows a double-digit improvement in sentence","creationdate":"2023-04-05T00:33:07+00:00","page_label":"18","author":"","subject":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","trapped":"/False","total_pages":35,"page":17,"keywords":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","producer":"pdfTeX-1.40.21","moddate":"2023-04-05T00:33:07+00:00","title":"","creator":"LaTeX with hyperref"}


created_at: 2026-06-08 08:17:00
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: dd9b6755-4fcc-4e7c-8e00-8c8835ed89b2
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{"keywords":"","author":"","chroma:document":"classiﬁcation accuracy and generates more diverse augmented samples while\nmaintaining its accuracy, but the original model is not ﬁne-tuned in the pa-\nper and suﬀers from a lack of domain knowledge, which may produce incorrect\naugmented data.\nMultimodal fusion\nChatGPT can currently only process natural language directly, but with a\ncross-modal encoder, it can combine natural language with cross-modal pro-\ncessing to provide solutions for intelligent transportation, healthcare, and other\nﬁelds.","moddate":"2023-04-05T00:33:07+00:00","page_label":"18","creationdate":"2023-04-05T00:33:07+00:00","total_pages":35,"subject":"","trapped":"/False","page":17,"title":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","creator":"LaTeX with hyperref","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","producer":"pdfTeX-1.40.21"}


created_at: 2026-06-08 08:17:00
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: 210393a1-23bc-494b-82b7-174e0ad91a91
encoding: FLOAT32
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{"page_label":"18","subject":"","trapped":"/False","producer":"pdfTeX-1.40.21","chroma:document":"ﬁelds.\nIn terms of multimodal data processing, Wu et al. [78] constructed a frame-\nwork that Visual ChatGPT integrates with diﬀerent Visual Foundation Models\n(VFMs) and then combines a series of hints to input visual information to Chat-\nGPT to solve visual problems.The paper shows examples of visual tasks such\nas removing or replacing certain objects from images, interconversion between\nimages and text, demonstrating the Visual ChatGPT has great potential and","title":"","page":17,"creator":"LaTeX with hyperref","creationdate":"2023-04-05T00:33:07+00:00","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","moddate":"2023-04-05T00:33:07+00:00","keywords":"","total_pages":35,"source":"./data/Summary of ChatGPTGPT-4 Research.pdf","author":""}


created_at: 2026-06-08 08:17:00
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: 3197afa8-ee35-41fa-a749-9703c2884b23
encoding: FLOAT32
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created_at: 2026-06-08 08:17:00
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: 5f627082-cb4e-43f9-90da-6d5847db0bf7
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{"keywords":"","creator":"LaTeX with hyperref","page_label":"18","page":17,"total_pages":35,"source":"./data/Summary of ChatGPTGPT-4 Research.pdf","author":"","subject":"","moddate":"2023-04-05T00:33:07+00:00","producer":"pdfTeX-1.40.21","trapped":"/False","title":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","creationdate":"2023-04-05T00:33:07+00:00","chroma:document":"keywords; introduced a use case concept of a smartphone-based framework for\nautomatic LLM failure report generation, which absorbs multiple data sources\ncaptured by cell phone sensors and then transfers the data to a language space\nfor text mining, inference and generation, and further outputs the key informa-\ntion needed to form a comprehensive fault report, demonstrating the potential\nof LLM for a variety of transportation tasks."}


created_at: 2026-06-08 08:17:00
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: 9ad4f8f1-8d55-4df2-b33e-c7194038a7fb
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{"creationdate":"2023-04-05T00:33:07+00:00","total_pages":35,"source":"./data/Summary of ChatGPTGPT-4 Research.pdf","title":"","trapped":"/False","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","creator":"LaTeX with hyperref","subject":"","keywords":"","page_label":"18","chroma:document":"of LLM for a variety of transportation tasks.\nNowadays, ChatGPT shows a wide range of applications in data visualiza-\ntion, information extraction, data enhancement, quality assessment, and multi-\nmodal data processing.But there are also issues on how to further utilize hints\nto eﬀectively interact with ChatGPT, lack of ability to process and analyze data\n18","producer":"pdfTeX-1.40.21","page":17,"author":"","moddate":"2023-04-05T00:33:07+00:00"}


created_at: 2026-06-08 08:17:00
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: a83997d1-55f5-4190-8560-ea219211175b
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{"page":18,"author":"","moddate":"2023-04-05T00:33:07+00:00","keywords":"","producer":"pdfTeX-1.40.21","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","total_pages":35,"title":"","subject":"","chroma:document":"from devices such as sensors, and data privacy and security.\nCueing Techniques\nCue engineering provides important support for eﬀective dialogue with large\nlanguage models.White et al. [80] proposed a framework for cueing models\napplicable to diﬀerent domains. This framework structures cues to interact\nwith LLMs by providing speciﬁc rules and guidelines. Also, this paper presents\na catalog of cueing patterns that have been applied to LLM interactions, as well","creator":"LaTeX with hyperref","creationdate":"2023-04-05T00:33:07+00:00","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","page_label":"19","trapped":"/False"}


created_at: 2026-06-08 08:17:00
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: f82b2e11-5412-4a25-afc6-6d82ffa4ad71
encoding: FLOAT32
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{"total_pages":35,"page_label":"19","trapped":"/False","chroma:document":"as speciﬁc examples with and without cues. The advantages of the combinability\nof prompting patterns are demonstrated, allowing users to interact with LLM\nmore eﬀectively, but patterns for reusable solutions and new ways to use LLM\nneed to be continuously explored.\n2.1.7 Human-ChatGPT Collaboration\nCollaboration between humans and machines is a process where humans and\nmachines work together to achieve a common goal. In such collaboration, hu-","title":"","creationdate":"2023-04-05T00:33:07+00:00","author":"","subject":"","page":18,"producer":"pdfTeX-1.40.21","creator":"LaTeX with hyperref","moddate":"2023-04-05T00:33:07+00:00","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","keywords":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf"}


created_at: 2026-06-08 08:17:00
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: 60a23396-dc05-489e-a81b-265c977804b5
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{"creator":"LaTeX with hyperref","keywords":"","title":"","author":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","page":18,"subject":"","trapped":"/False","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","creationdate":"2023-04-05T00:33:07+00:00","chroma:document":"mans provide domain expertise, creativity, and decision-making abilities, while\nmachines provide automation, scalability, and computing power. ChatGPT is\nan advanced natural language processing model that can understand and gen-\nerate human-like language, thereby reducing communication costs. Its ability\nto process and generate natural language makes it an ideal partner for human\ncollaboration. ChatGPT can oﬀer relevant suggestions, complete tasks based","page_label":"19","total_pages":35,"moddate":"2023-04-05T00:33:07+00:00","producer":"pdfTeX-1.40.21"}


created_at: 2026-06-08 08:17:00
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: e14ee560-89bb-41c8-ac2d-2048a9c9e487
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{"ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","trapped":"/False","page":18,"creator":"LaTeX with hyperref","moddate":"2023-04-05T00:33:07+00:00","total_pages":35,"creationdate":"2023-04-05T00:33:07+00:00","page_label":"19","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","chroma:document":"on human input, and enhance human productivity and creativity. It can learn\nfrom human feedback and adapt to new tasks and domains, further improv-\ning its performance in human-machine collaboration. ChatGPT’s capability to\ncomprehend natural language and produce appropriate responses makes it a\nvaluable tool for various collaboration applications, as demonstrated by several\nstudies in the literature we have gathered.\nAhmad et al. [81] proposed a method for human-machine collaboration us-","producer":"pdfTeX-1.40.21","keywords":"","author":"","subject":"","title":""}


created_at: 2026-06-08 08:17:00
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: 55bbacb9-c98b-40e0-aaaf-d2c496e964f2
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{"ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","creationdate":"2023-04-05T00:33:07+00:00","producer":"pdfTeX-1.40.21","keywords":"","page_label":"19","title":"","subject":"","trapped":"/False","author":"","page":18,"chroma:document":"ing ChatGPT to create software architecture. This method transforms software\nstories (created by software architects based on application scenarios) into feasi-\nble software architecture diagrams through continuous interaction between the\nsoftware architect and ChatGPT. During the evaluation stage, ChatGPT uses\nthe Software Architecture Analysis Method (SAAM) to evaluate each compo-\nnent in the software architecture and generate evaluation reports. This method","creator":"LaTeX with hyperref","moddate":"2023-04-05T00:33:07+00:00","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","total_pages":35}


created_at: 2026-06-08 08:17:00
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: df0ddb93-78bf-4ff0-985a-3d439bca19f0
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{"trapped":"/False","page_label":"19","chroma:document":"eﬃciently utilizes the knowledge and supervision of the architect with the ca-\npabilities of ChatGPT to collaboratively build software-intensive systems and\nservices. Lanzi et al. [82] proposed a collaborative design framework that com-\nbines interactive evolution and ChatGPT to simulate typical human design pro-\ncesses. Humans collaborate with large language models (such as ChatGPT) to\nrecombine and transform ideas, and use genetic algorithms to iterate through","producer":"pdfTeX-1.40.21","total_pages":35,"keywords":"","creationdate":"2023-04-05T00:33:07+00:00","title":"","creator":"LaTeX with hyperref","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","subject":"","moddate":"2023-04-05T00:33:07+00:00","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","author":"","page":18}


created_at: 2026-06-08 08:17:00
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: cbcf58b6-dc94-4e59-9144-9ed7bd3ad8f8
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{"creationdate":"2023-04-05T00:33:07+00:00","subject":"","chroma:document":"complex creative tasks. The results of three game design tasks showed that the\nframework received positive feedback from game designers. The framework has\ngood reusability and can be applied to any design task that can be described in\nfree text form.\n19","producer":"pdfTeX-1.40.21","author":"","page":18,"page_label":"19","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","keywords":"","moddate":"2023-04-05T00:33:07+00:00","total_pages":35,"title":"","trapped":"/False","creator":"LaTeX with hyperref","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2"}


created_at: 2026-06-08 08:17:00
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: 67a167fe-78bc-40d8-adb3-44ad49c608b2
encoding: FLOAT32
metadata:
{"title":"","producer":"pdfTeX-1.40.21","keywords":"","creator":"LaTeX with hyperref","subject":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","moddate":"2023-04-05T00:33:07+00:00","author":"","chroma:document":"In the future, ChatGPT’s ability to understand nonverbal cues such as tone\nof voice and body language can be enhanced, enabling it to better understand\nhuman thoughts and interact with people more eﬀectively.\n2.1.8 ChatGPT Integration\nIntegration refers to combining diﬀerent systems or software components to\nachieve a common goal. ChatGPT can be integrated as a part of a whole or\nact as an integration tool to enable seamless communication between diﬀerent","trapped":"/False","creationdate":"2023-04-05T00:33:07+00:00","total_pages":35,"page":19,"ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","page_label":"20"}


created_at: 2026-06-08 08:17:00
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: 7f57ea1d-3a21-4c9a-bb33-c554a149f173
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{"chroma:document":"systems. Its natural language processing ability makes it easier for non-technical\nusers to interact with systems, reducing the need for specialized knowledge or\ntraining. Some studies in the literature we collected have already demonstrated\nthis.\nTreude et al. [39] integrated ChatGPT into the prototype of ”GPTCOM-\nCARE” to address programming query problems. This integration allowed for\nthe generation of multiple source code solutions for the same query, which in-","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","keywords":"","producer":"pdfTeX-1.40.21","trapped":"/False","author":"","subject":"","creator":"LaTeX with hyperref","total_pages":35,"title":"","creationdate":"2023-04-05T00:33:07+00:00","page":19,"ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","moddate":"2023-04-05T00:33:07+00:00","page_label":"20"}


created_at: 2026-06-08 08:17:00
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: 7e4d69a9-3568-49d0-8024-89848fd2f3d1
encoding: FLOAT32
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{"creator":"LaTeX with hyperref","title":"","moddate":"2023-04-05T00:33:07+00:00","total_pages":35,"author":"","keywords":"","page":19,"chroma:document":"creased the eﬃciency of software development. The results of their study demon-\nstrated the eﬀectiveness of using ChatGPT to improve the quality and diversity\nof code solutions, ultimately reducing the amount of time and eﬀort required for\nsoftware development.Wang et al. [83] proposed the chatCAD method, which\nutilizes large language models (LLMs) such as ChatGPT to enhance the out-\nput of multiple CAD networks for medical images, including diagnosis, lesion","creationdate":"2023-04-05T00:33:07+00:00","producer":"pdfTeX-1.40.21","page_label":"20","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","trapped":"/False","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","subject":""}


created_at: 2026-06-08 08:17:00
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: 699a1b61-8002-4e14-b6a3-f705fa214cba
encoding: FLOAT32
metadata:
{"ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","author":"","title":"","page_label":"20","moddate":"2023-04-05T00:33:07+00:00","chroma:document":"segmentation, and report generation networks. The method generates sugges-\ntions in the form of a chat dialogue. The authors tested the eﬀectiveness of\nthe method on a randomly selected set of 300 cases from the MIMIC-CXR\ndataset, which included 50 cases each of cardiomegaly, edema, consolidation,\natelectasis, pleural eﬀusion, and no ﬁndings. Compared to CvT2DistilGPT2\nand R2GenCMN, chatCAD showed signiﬁcant advantages in RC and F1, while\nonly performing weaker than R2GenCMN in PR.","trapped":"/False","creationdate":"2023-04-05T00:33:07+00:00","creator":"LaTeX with hyperref","subject":"","page":19,"total_pages":35,"source":"./data/Summary of ChatGPTGPT-4 Research.pdf","producer":"pdfTeX-1.40.21","keywords":""}


created_at: 2026-06-08 08:17:00
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: 517f278a-52b3-4c15-ba56-1113eb056486
encoding: FLOAT32
metadata:
{"author":"","creationdate":"2023-04-05T00:33:07+00:00","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","page":19,"trapped":"/False","subject":"","total_pages":35,"producer":"pdfTeX-1.40.21","chroma:document":"only performing weaker than R2GenCMN in PR.\nIntegrating ChatGPT into applications will still present challenges. Firstly,\nChatGPT’s performance may be aﬀected by language barriers or diﬀerences\nin terminology between diﬀerent systems. Additionally, ChatGPT’s responses\nare not always deterministic, which poses a challenge when integrating with\nsystems that require precise and reproducible results. Finally, the processing","keywords":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","page_label":"20","moddate":"2023-04-05T00:33:07+00:00","creator":"LaTeX with hyperref","title":""}


created_at: 2026-06-08 08:17:00
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: c0159a25-4871-4503-9039-44130726d638
encoding: FLOAT32
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{"chroma:document":"time of ChatGPT is slow for integration tasks involving time-sensitive data such\nas traﬃc, which is a limitation in time-critical environments.\n2.2 AI Ethics\nSince the advent of ChatGPT, this powerful natural language processing model\nhas not only brought great convenience to people but also triggered more crisis-\naware thinking. Some researchers have started to hypothesize and study the\npotential negative impacts of ChatGPT. This proactive research provides good","title":"","moddate":"2023-04-05T00:33:07+00:00","creationdate":"2023-04-05T00:33:07+00:00","page":19,"ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","total_pages":35,"source":"./data/Summary of ChatGPTGPT-4 Research.pdf","subject":"","producer":"pdfTeX-1.40.21","author":"","keywords":"","creator":"LaTeX with hyperref","page_label":"20","trapped":"/False"}


created_at: 2026-06-08 08:17:00
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: d22934ed-3916-4f7d-9d3e-41416aec4da3
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{"source":"./data/Summary of ChatGPTGPT-4 Research.pdf","subject":"","chroma:document":"proposals for standardized construction to address future AI abuse issues.\nRegarding the evaluation of ChatGPT’s own political and ethical tendencies,\nHartmann et al. [84] used Wahl-O-Mat, one of the most commonly used voting\n20","trapped":"/False","page":19,"creator":"LaTeX with hyperref","moddate":"2023-04-05T00:33:07+00:00","author":"","creationdate":"2023-04-05T00:33:07+00:00","title":"","producer":"pdfTeX-1.40.21","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","total_pages":35,"page_label":"20","keywords":""}


created_at: 2026-06-08 08:17:00
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: 10b602a3-7c67-4c3f-bb57-b1a707e483d6
encoding: FLOAT32
metadata:
{"title":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","producer":"pdfTeX-1.40.21","creator":"LaTeX with hyperref","keywords":"","subject":"","chroma:document":"advice applications in the world, to show ChatGPT political statements from\ndiﬀerent parties, forcing it to make choices of agree, disagree, or neutral. The\nresults indicated that ChatGPT has a pro-environment, left-wing liberal ide-\nology, which was also conﬁrmed in the nation-state agnostic political compass\ntest. Another study (referenced as [85]) examined ChatGPT’s moral standards\nby repeatedly asking it diﬀerent versions of the trolley problem, and found that","total_pages":35,"moddate":"2023-04-05T00:33:07+00:00","page_label":"21","page":20,"trapped":"/False","author":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","creationdate":"2023-04-05T00:33:07+00:00"}


created_at: 2026-06-08 08:17:00
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: 56c994a5-f727-4f2c-b60d-c72bb213389a
encoding: FLOAT32
metadata:
{"producer":"pdfTeX-1.40.21","author":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","creationdate":"2023-04-05T00:33:07+00:00","page":20,"subject":"","chroma:document":"ChatGPT gave answers with diﬀerent moral orientations, lacking a ﬁrm moral\nstance. A subsequent test also found that ChatGPT’s lack of consistency could\naﬀect people’s moral judgments. Additionally, Borji et al. [86] demonstrated\nChatGPT’s inconsistency in reasoning, factual errors, mathematics, coding, and\nbias across eleven related aspects. These ﬁndings highlight ChatGPT’s inher-\nent traits and limitations, and people should be aware of their potential impact","creator":"LaTeX with hyperref","title":"","keywords":"","page_label":"21","moddate":"2023-04-05T00:33:07+00:00","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","trapped":"/False","total_pages":35}


created_at: 2026-06-08 08:17:00
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: 5778dcc7-7df3-4538-8ec2-c18a3cc8ff03
encoding: FLOAT32
metadata:
{"trapped":"/False","creator":"LaTeX with hyperref","title":"","total_pages":35,"author":"","moddate":"2023-04-05T00:33:07+00:00","subject":"","keywords":"","page_label":"21","creationdate":"2023-04-05T00:33:07+00:00","chroma:document":"when seeking advice from ChatGPT.\nRegarding relevant policies and regulations, Hacker et al. [87] discussed the\nnature and rules of large generative AI models, including ChatGPT, which are\nrapidly changing the way we communicate, explain, and create. The author\nsuggested that diﬀerent stakeholders in the value chain should take regulatory\nresponsibility and deploy four strategies to tailor more comprehensive laws for","page":20,"producer":"pdfTeX-1.40.21","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","source":"./data/Summary of ChatGPTGPT-4 Research.pdf"}


created_at: 2026-06-08 08:17:00
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: 032557ae-739e-4763-856f-60203518ecfa
encoding: FLOAT32
metadata:
{"page":20,"total_pages":35,"trapped":"/False","moddate":"2023-04-05T00:33:07+00:00","creationdate":"2023-04-05T00:33:07+00:00","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","creator":"LaTeX with hyperref","subject":"","author":"","page_label":"21","producer":"pdfTeX-1.40.21","title":"","keywords":"","chroma:document":"the beneﬁt of society. Another study (referenced as [88]) criticized the European\nCommission’s proposal on AI responsibility and suggested revising the proposed\nAI responsibility framework to ensure eﬀective compensation while promoting\ninnovation, legal certainty, and sustainable AI regulation. A policy framework\nwas proposed (referenced as [89]) to customize LLMs, such as ChatGPT, in a so-\ncially acceptable and safe manner, emphasizing the need to align large language","source":"./data/Summary of ChatGPTGPT-4 Research.pdf"}


created_at: 2026-06-08 08:17:00
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: b3d265fc-9980-4d9b-a362-225d93ee3d85
encoding: FLOAT32
metadata:
{"creator":"LaTeX with hyperref","producer":"pdfTeX-1.40.21","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","author":"","keywords":"","page_label":"21","page":20,"source":"./data/Summary of ChatGPTGPT-4 Research.pdf","total_pages":35,"creationdate":"2023-04-05T00:33:07+00:00","title":"","trapped":"/False","chroma:document":"models (LLMs) with human preferences.\nThe political and ethical tendencies of ChatGPT could inﬂuence users’ be-\nhavior and decision-making to some extent. However, some studies have con-\nducted in-depth research on the use of norms and limitations, which could enable\nhumans to use ChatGPT more reasonably and safely.\n2.3 Evaluation\n2.3.1 Comparison of ChatGPT with existing popular models\nWe use publicly available datasets to comprehensively evaluate the strengths","subject":"","moddate":"2023-04-05T00:33:07+00:00"}


created_at: 2026-06-08 08:17:00
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: 52f5192d-3442-48c0-ab98-2fb393938d45
encoding: FLOAT32
metadata:
{"ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","trapped":"/False","page_label":"21","creator":"LaTeX with hyperref","chroma:document":"and limitations of ChatGPT. Reference [90] evaluates the technical performance\nof ChatGPT in multitask, multilingual, and multimodal aspects based on 23\nstandard public datasets and newly designed multimodal datasets, including\neight diﬀerent common natural language processing application tasks. The ex-\nperimental results show that, in terms of multitasking, ChatGPT outperforms\nvarious state-of-the-art zero-shot learning large language models in most tasks,","title":"","producer":"pdfTeX-1.40.21","keywords":"","total_pages":35,"page":20,"author":"","subject":"","creationdate":"2023-04-05T00:33:07+00:00","moddate":"2023-04-05T00:33:07+00:00"}


created_at: 2026-06-08 08:17:00
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: 6cec7b1d-9d9a-407b-ae06-0b359a85fd7b
encoding: FLOAT32
metadata:
{"keywords":"","total_pages":35,"page":20,"trapped":"/False","page_label":"21","title":"","subject":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","moddate":"2023-04-05T00:33:07+00:00","author":"","creator":"LaTeX with hyperref","producer":"pdfTeX-1.40.21","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","creationdate":"2023-04-05T00:33:07+00:00","chroma:document":"and even outperforms ﬁne-tuned task-speciﬁc models in some individual tasks.\nIn terms of multilingualism, we found that ChatGPT cannot be applied to low-\nresource languages because it cannot understand the language and generate\ntranslations for that language. In terms of multimodality, ChatGPT’s ability is\nstill basic compared to specialized language-visual models.\n21"}


created_at: 2026-06-08 08:17:00
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: 46156cb2-735d-4935-b680-df2d17ec92a6
encoding: FLOAT32
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{"creationdate":"2023-04-05T00:33:07+00:00","creator":"LaTeX with hyperref","chroma:document":"In terms of stability, reference [91] concludes that ChatGPT’s performance is\nalways lower than SOTA, the current state-of-the-art model, in almost all tasks.\nThis means that as a general model, ChatGPT has never reached the level of the\nbest existing models. Experimental data shows that the average quality of the\nSOTA model is 73.7%, while the average quality of the ChatGPT model is only\n56.5%. At the same time, ChatGPT’s stability is poor: the standard deviation","page_label":"22","keywords":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","trapped":"/False","producer":"pdfTeX-1.40.21","subject":"","page":21,"title":"","author":"","moddate":"2023-04-05T00:33:07+00:00","total_pages":35}


created_at: 2026-06-08 08:17:00
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: 5245e545-d84f-45d0-84ab-ce30408ae2d2
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{"total_pages":35,"chroma:document":"of its performance is 23.3%, while the SOTA model’s standard deviation is\nonly 16.7%. This non-deterministic behavior exhibited by ChatGPT could be\na serious drawback in some problems.\nSimilarly, Qin et al. [92] conducted a comprehensive evaluation of whether\nChatGPT is a qualiﬁed general natural language processing task solver. The ex-\nperiment analyzed ChatGPT’s zero-shot learning ability based on 20 commonly\nused public datasets covering 7 representative task categories. Below, we will","producer":"pdfTeX-1.40.21","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","moddate":"2023-04-05T00:33:07+00:00","page_label":"22","creationdate":"2023-04-05T00:33:07+00:00","author":"","title":"","creator":"LaTeX with hyperref","keywords":"","trapped":"/False","subject":"","page":21,"source":"./data/Summary of ChatGPTGPT-4 Research.pdf"}


created_at: 2026-06-08 08:17:00
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: bdc782fb-f5ec-400f-a1ca-240a2de158f9
encoding: FLOAT32
metadata:
{"creator":"LaTeX with hyperref","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","keywords":"","page":21,"source":"./data/Summary of ChatGPTGPT-4 Research.pdf","total_pages":35,"title":"","page_label":"22","trapped":"/False","chroma:document":"analyze ChatGPT’s performance on each task:\nIn terms of reasoning tasks, ChatGPT performs average on mathematical\nsymbol, commonsense causal, and logical reasoning tasks, but performs well\nin arithmetic reasoning [92]. That is to say, ChatGPT’s abilities vary among\ndiﬀerent types of reasoning tasks. In terms of logical reasoning, ChatGPT’s\ndeductive and abductive reasoning are superior to inductive reasoning, while\nin other reasoning tasks, such as analogy, causal and commonsense reasoning,","author":"","moddate":"2023-04-05T00:33:07+00:00","creationdate":"2023-04-05T00:33:07+00:00","producer":"pdfTeX-1.40.21","subject":""}


created_at: 2026-06-08 08:17:00
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: 348bc761-6c81-45df-a861-f18eaa45903e
encoding: FLOAT32
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{"page_label":"22","trapped":"/False","total_pages":35,"author":"","producer":"pdfTeX-1.40.21","title":"","moddate":"2023-04-05T00:33:07+00:00","chroma:document":"ChatGPT performs well [90].\nIn terms of sentiment analysis task, ChatGPT performs similarly to GPT-3.5\nand bert-style models [92, 93]. However, according to literature [91], ChatGPT\nhas losses not exceeding 25% on most tasks, except for three relatively sub-\njective emotion perception tasks where it performs poorly. If we remove these\ntasks to calculate the average quality of the two models, we ﬁnd that the SOTA\nmethod has an average quality of 80%, while the ChatGPT method has an av-","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","creator":"LaTeX with hyperref","keywords":"","creationdate":"2023-04-05T00:33:07+00:00","page":21,"subject":""}


created_at: 2026-06-08 08:17:00
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: 981d3019-6cb1-478b-8390-02cedc15450b
encoding: FLOAT32
metadata:
{"source":"./data/Summary of ChatGPTGPT-4 Research.pdf","moddate":"2023-04-05T00:33:07+00:00","producer":"pdfTeX-1.40.21","page":21,"title":"","creationdate":"2023-04-05T00:33:07+00:00","keywords":"","trapped":"/False","author":"","total_pages":35,"ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","subject":"","creator":"LaTeX with hyperref","chroma:document":"erage quality of 69.7%. That is to say, ChatGPT performs well on all tasks\nexcept for emotion-related tasks, and can handle most of the problems we con-\nsider. However, overall, its performance is lower than the SOTA model based\non experimental data, but the diﬀerence between the two is not very large.\nIn other tasks, according to literature [92], ChatGPT performs well in nat-\nural language inference, i.e., the task of inferring sentence relationships, and its","page_label":"22"}


created_at: 2026-06-08 08:17:00
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: cf9b4670-2ec6-4cf9-9b89-29e2ff2acc8e
encoding: FLOAT32
metadata:
{"trapped":"/False","page":21,"producer":"pdfTeX-1.40.21","moddate":"2023-04-05T00:33:07+00:00","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","author":"","chroma:document":"performance on this task is signiﬁcantly better than all bert-style models [93].\nHowever, while ChatGPT performs well on inference tasks, it may produce some\nself-contradictory or unreasonable responses, which is its potential limitation.\nIn question-answering, dialogue, and summarization tasks, ChatGPT performs\nbetter than the GPT-3.5 model [92], especially in the question-answering task,\nwhere its performance is comparable to bert-style models [93]. Therefore, we","creator":"LaTeX with hyperref","subject":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","page_label":"22","total_pages":35,"keywords":"","creationdate":"2023-04-05T00:33:07+00:00","title":""}


created_at: 2026-06-08 08:17:00
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: 27200441-92d1-4916-a0cc-af1681e8ecc9
encoding: FLOAT32
metadata:
{"moddate":"2023-04-05T00:33:07+00:00","creator":"LaTeX with hyperref","creationdate":"2023-04-05T00:33:07+00:00","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","keywords":"","chroma:document":"have demonstrated that ChatGPT is a qualiﬁed general-purpose model.\nHowever, ChatGPT also has limitations in many aspects. Firstly, it lacks the\nability to handle non-textual semantic reasoning tasks such as mathematical,\ntemporal, and spatial reasoning, and it performs poorly in multi-hop reasoning\n[90]. Secondly, ChatGPT is not good at solving named entity recognition tasks\n[92]. Furthermore, ChatGPT performs poorly in handling tasks involving nega-","producer":"pdfTeX-1.40.21","title":"","page":21,"source":"./data/Summary of ChatGPTGPT-4 Research.pdf","page_label":"22","subject":"","author":"","total_pages":35,"trapped":"/False"}


created_at: 2026-06-08 08:17:00
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: 62db2dd4-9193-4d04-805b-e0153f4dd173
encoding: FLOAT32
metadata:
{"trapped":"/False","creationdate":"2023-04-05T00:33:07+00:00","total_pages":35,"page_label":"22","title":"","creator":"LaTeX with hyperref","producer":"pdfTeX-1.40.21","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","author":"","subject":"","moddate":"2023-04-05T00:33:07+00:00","chroma:document":"tive connotations and neutral similarity [93]. Finally, these conclusions indicate\n22","page":21,"keywords":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf"}


created_at: 2026-06-08 08:17:00
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: adc07517-b795-4a1b-9af3-e7aa4fac5926
encoding: FLOAT32
metadata:
{"producer":"pdfTeX-1.40.21","author":"","keywords":"","title":"","subject":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","creator":"LaTeX with hyperref","creationdate":"2023-04-05T00:33:07+00:00","trapped":"/False","page_label":"23","moddate":"2023-04-05T00:33:07+00:00","chroma:document":"that, like other large pre-trained language models, ChatGPT has limitations in\ncompleting complex reasoning tasks.\nIn summary, ChatGPT’s zero-shot performance is comparable to ﬁne-tuned\nbert and GPT-3.5 models, and with the help of advanced prompting strategies,\nChatGPT can demonstrate better comprehension abilities. However, it still\ncannot outperform the current SOTA models.\n2.3.2 The possibility of using ChatGPT for plagiarism and cheating","page":22,"total_pages":35,"source":"./data/Summary of ChatGPTGPT-4 Research.pdf"}


created_at: 2026-06-08 08:17:00
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: 06785068-1e5f-43fa-9cd0-231d551b555b
encoding: FLOAT32
metadata:
{"total_pages":35,"creationdate":"2023-04-05T00:33:07+00:00","subject":"","author":"","keywords":"","page_label":"23","title":"","producer":"pdfTeX-1.40.21","creator":"LaTeX with hyperref","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","chroma:document":"In response to the possibility of ChatGPT being used for plagiarism and cheat-\ning, Zhou et al. [94] reﬂected on the current state of development of artiﬁcial in-\ntelligence like ChatGPT. As ChatGPT becomes increasingly easy to obtain and\nscalable in text generation, there is a high likelihood that these technologies will\nbe used for plagiarism, including scientiﬁc literature and news sources, posing\na great threat to the credibility of various forms of news media and academic","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","trapped":"/False","page":22,"moddate":"2023-04-05T00:33:07+00:00"}


created_at: 2026-06-08 08:17:00
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: bfc4c07f-8cf9-4356-823d-acb590e19c91
encoding: FLOAT32
metadata:
{"moddate":"2023-04-05T00:33:07+00:00","keywords":"","author":"","producer":"pdfTeX-1.40.21","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","trapped":"/False","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","page":22,"subject":"","total_pages":35,"chroma:document":"articles. Some scholars are concerned that the end of paper as a meaningful\nevaluation tool may be approaching [95, 96], as ChatGPT can easily generate\npersuasive paragraphs, chapters, and papers on any given topic. Additionally,\nit will exacerbate plagiarism issues in many ﬁelds such as education, medicine,\nand law [10], and may be used for cheating in academic exams [97]. Deﬁnitional\nrecognition technology is a relatively eﬀective method for detecting plagiarism,","creationdate":"2023-04-05T00:33:07+00:00","title":"","creator":"LaTeX with hyperref","page_label":"23"}


created_at: 2026-06-08 08:17:00
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: a517b6aa-9f86-4a00-8f97-7097e453650a
encoding: FLOAT32
metadata:
{"ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","trapped":"/False","author":"","page_label":"23","moddate":"2023-04-05T00:33:07+00:00","producer":"pdfTeX-1.40.21","creator":"LaTeX with hyperref","creationdate":"2023-04-05T00:33:07+00:00","chroma:document":"and the deﬁnitional typology proposed in [94] can alleviate people’s concerns by\nbeing used to construct new datasets. Susnjak [97] proposed a solution to the\npossibility of large language models like ChatGPT being used for exam cheat-\ning: guiding ChatGPT to generate some critical thinking problems through\nquestioning, then providing answers and critically evaluating them. Analysis of\nChatGPT shows that it exhibits critical thinking, can generate highly realistic","keywords":"","page":22,"subject":"","total_pages":35,"title":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf"}


created_at: 2026-06-08 08:17:00
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: 9bb0b9ed-0f7c-40e0-b7a1-a4ae5e72abf0
encoding: FLOAT32
metadata:
{"title":"","moddate":"2023-04-05T00:33:07+00:00","trapped":"/False","producer":"pdfTeX-1.40.21","keywords":"","author":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","page_label":"23","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","chroma:document":"text in terms of accuracy, relevance, depth, breadth, logic, persuasiveness, and\noriginality. Therefore, educators must be aware of the possibility of ChatGPT\nbeing used for exam cheating and take measures to combat cheating behavior\nto ensure the fairness of online exams.\n2.3.3 Feedback from ChatGPT users\nIn response to feedback from ChatGPT users, Haque et al. [98] conducted a\nmixed-methods study using 10,732 early ChatGPT user tweets. The authors","total_pages":35,"creationdate":"2023-04-05T00:33:07+00:00","creator":"LaTeX with hyperref","subject":"","page":22}


created_at: 2026-06-08 08:17:00
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: 622735ea-d26d-47ae-9129-8307202bdff6
encoding: FLOAT32
metadata:
{"producer":"pdfTeX-1.40.21","creator":"LaTeX with hyperref","title":"","trapped":"/False","creationdate":"2023-04-05T00:33:07+00:00","page_label":"23","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","chroma:document":"extracted Twitter data using Python and Twitter API and constructed the\nChatGPTTweet dataset, which contains 18k tweets. For each tweet, the au-\nthors collected information on text content, user location, occupation, veriﬁca-\ntion status, date of publication, and tags. Based on this dataset, the authors\nstudied the characteristics of early ChatGPT users, discussion topics related to\nChatGPT on Twitter, and the sentiment of Twitter users toward ChatGPT.","total_pages":35,"subject":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","moddate":"2023-04-05T00:33:07+00:00","keywords":"","page":22,"author":""}


created_at: 2026-06-08 08:17:00
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: 222a94d0-171c-49a9-9077-ea4544e9b034
encoding: FLOAT32
metadata:
{"trapped":"/False","subject":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","page_label":"23","creationdate":"2023-04-05T00:33:07+00:00","page":22,"moddate":"2023-04-05T00:33:07+00:00","chroma:document":"For RQ1, the authors found that early ChatGPT users had a diverse and wide\nrange of occupational backgrounds and geographical locations. For RQ2, the\nauthors identiﬁed nine topics related to ChatGPT, including its impact on soft-\nware development, entertainment and creativity, natural language processing,\n23","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","creator":"LaTeX with hyperref","total_pages":35,"producer":"pdfTeX-1.40.21","keywords":"","title":"","author":""}


created_at: 2026-06-08 08:17:00
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: e1f3fdb9-ea7f-44e0-ab2d-f4c03cdf9395
encoding: FLOAT32
metadata:
{"producer":"pdfTeX-1.40.21","subject":"","keywords":"","author":"","page":23,"trapped":"/False","chroma:document":"education, chatbot intelligence, business development, search engines, question-\nanswering tests, and future careers and opportunities. For RQ3, most early\nusers expressed positive sentiment toward topics such as software development\nand creativity, while only a few expressed concern about the potential misuse\nof ChatGPT.\n2.3.4 Adverse eﬀects of ChatGPT on users\nRegarding the negative eﬀects of ChatGPT on users, Luan et al. [99] studied","creationdate":"2023-04-05T00:33:07+00:00","page_label":"24","title":"","creator":"LaTeX with hyperref","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","total_pages":35,"moddate":"2023-04-05T00:33:07+00:00"}


created_at: 2026-06-08 08:17:00
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: 688590f0-5311-4bad-9582-52af8e04335b
encoding: FLOAT32
metadata:
{"total_pages":35,"source":"./data/Summary of ChatGPTGPT-4 Research.pdf","page_label":"24","moddate":"2023-04-05T00:33:07+00:00","keywords":"","creator":"LaTeX with hyperref","creationdate":"2023-04-05T00:33:07+00:00","page":23,"chroma:document":"the psychological principles of ChatGPT, delved into the factors that attract\nusers’ attention, and revealed the impact of these factors on future learning.\nIn the post-pandemic era, teachers and students are both facing uncertainty\nin the teaching process and job pressures. Under these common constraints of\neducation and employment, educators and students must re-evaluate current\neducational methods and outcomes, as well as students’ future career devel-","title":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","trapped":"/False","subject":"","producer":"pdfTeX-1.40.21","author":""}


created_at: 2026-06-08 08:17:00
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: 98304d92-a008-4ac2-b959-06b0828148ad
encoding: FLOAT32
metadata:
{"page_label":"24","title":"","chroma:document":"opment. Through question-and-answer exchanges with ChatGPT, people can\neasily obtain appropriate solutions or key information, thereby enhancing their\nmotivation, eliminating anxiety in learning, improving interest, and achieving\npsychological satisfaction. Subhash et al. [100] explored whether large language\nmodels have the ability to reverse user preferences. With the development of\npre-trained large language models, people are increasingly concerned about the","moddate":"2023-04-05T00:33:07+00:00","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","total_pages":35,"source":"./data/Summary of ChatGPTGPT-4 Research.pdf","keywords":"","trapped":"/False","page":23,"producer":"pdfTeX-1.40.21","author":"","subject":"","creator":"LaTeX with hyperref","creationdate":"2023-04-05T00:33:07+00:00"}


created_at: 2026-06-08 08:17:00
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: 37570c89-7cdc-4b5d-8cd2-347efdc3baea
encoding: FLOAT32
metadata:
{"ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","producer":"pdfTeX-1.40.21","page_label":"24","chroma:document":"ability of these models to inﬂuence, persuade, and potentially manipulate user\npreferences in extreme cases. Therefore, the literature [100] roughly qualita-\ntively analyzed that adversarial behavior does lead to potential changes in user\npreferences and behaviors in dialogue systems. If we want to further quanti-\ntatively analyze the ability of large language models in this regard, additional\nstatistical summary techniques need to be used for future research.\n3 Discussion\n3.1 Limitations","title":"","creationdate":"2023-04-05T00:33:07+00:00","moddate":"2023-04-05T00:33:07+00:00","trapped":"/False","keywords":"","page":23,"subject":"","creator":"LaTeX with hyperref","total_pages":35,"source":"./data/Summary of ChatGPTGPT-4 Research.pdf","author":""}


created_at: 2026-06-08 08:17:00
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: 0f54cd7c-46fb-4d03-83e9-829c3ab812ef
encoding: FLOAT32
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{"page_label":"24","chroma:document":"3 Discussion\n3.1 Limitations\nDespite the remarkable capabilities of ChatGPT and GPT-4, it still faces certain\nlimitations. Some of these limitations include:\nOutdated Knowledge\nThe current models are trained on historical data (up to 2021), thereby\nlacking real-time comprehension of current aﬀairs. This is a critical concern\nin today’s information-explosion era, as the reliability of prior knowledge bases\nprogressively diminishes, potentially yielding inaccurate responses, especially in","producer":"pdfTeX-1.40.21","title":"","creationdate":"2023-04-05T00:33:07+00:00","subject":"","trapped":"/False","author":"","total_pages":35,"creator":"LaTeX with hyperref","page":23,"ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","keywords":"","moddate":"2023-04-05T00:33:07+00:00"}


created_at: 2026-06-08 08:17:00
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: 9334d439-ee76-44bb-96e2-ee01488fa7c5
encoding: FLOAT32
metadata:
{"moddate":"2023-04-05T00:33:07+00:00","creator":"LaTeX with hyperref","total_pages":35,"creationdate":"2023-04-05T00:33:07+00:00","page_label":"24","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","author":"","page":23,"subject":"","title":"","keywords":"","trapped":"/False","producer":"pdfTeX-1.40.21","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","chroma:document":"rapidly evolving domains such as jurisprudence and technology. Additionally,\nthese models are incapable of fact-checking while the training data is composed\nof content from various sources, some of which may be unreliable, which may\nresult in seemingly plausible yet nonsensical responses.\n24"}


created_at: 2026-06-08 08:17:00
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: 993ff68c-06ab-47e7-b5ed-ec6931eee8c6
encoding: FLOAT32
metadata:
{"ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","title":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","total_pages":35,"moddate":"2023-04-05T00:33:07+00:00","producer":"pdfTeX-1.40.21","chroma:document":"Insuﬃcient Understanding\nWhile these models can interpret the majority of inquiries and contextual\nsituations, they occasionally encounter comprehension biases when addressing\nambiguous or contextually complex queries. Furthermore, in certain specialized\nﬁelds, the abundance of unique abbreviation exacerbates the models’ under-\nstanding challenges, resulting in incorrect and vacuous responses.\nEnergy Consumption\nThroughout the training and inference stages, these large-scale models re-","keywords":"","trapped":"/False","creationdate":"2023-04-05T00:33:07+00:00","subject":"","creator":"LaTeX with hyperref","page":24,"author":"","page_label":"25"}


created_at: 2026-06-08 08:17:00
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
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{"author":"","trapped":"/False","total_pages":35,"moddate":"2023-04-05T00:33:07+00:00","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","producer":"pdfTeX-1.40.21","page_label":"25","keywords":"","creator":"LaTeX with hyperref","page":24,"ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","creationdate":"2023-04-05T00:33:07+00:00","chroma:document":"quire signiﬁcant computational resources and electrical power, resulting in ele-\nvated energy consumption and signiﬁcant carbon emissions. Consequently, this\nrestricts their deployment and practical applications.\nMalicious Usage\nDespite OpenAI implementing a series of restrictions to mitigate model tox-\nicity, instances of users evading these constraints through meticulously designed\nprompts have emerged, inducing the model to produce unhealthy content or","subject":"","title":""}


created_at: 2026-06-08 08:17:00
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
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{"total_pages":35,"creator":"LaTeX with hyperref","page_label":"25","chroma:document":"even using it for illicit commercial purposes.\nBias and Discrimination\nDue to the inﬂuence of pre-training data, the models exhibit biases in polit-\nical, ideological, and other areas. The application of LLMs in public domains,\nsuch as education and publicity, should be approached with extreme caution.\nPrivacy and Data Security\nConcurrent with the expansion of users, protecting user privacy and data\nsecurity becomes increasingly important. In fact, ChatGPT was banned in","author":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","keywords":"","moddate":"2023-04-05T00:33:07+00:00","producer":"pdfTeX-1.40.21","title":"","subject":"","trapped":"/False","page":24,"creationdate":"2023-04-05T00:33:07+00:00"}


created_at: 2026-06-08 08:17:00
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: a24c746f-0ead-4e2e-91c3-3437f5ad5aaf
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{"moddate":"2023-04-05T00:33:07+00:00","subject":"","creationdate":"2023-04-05T00:33:07+00:00","page":24,"source":"./data/Summary of ChatGPTGPT-4 Research.pdf","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","author":"","keywords":"","total_pages":35,"producer":"pdfTeX-1.40.21","chroma:document":"Italy in early April due to privacy concerns. This is particularly relevant given\nthe models’ extensive collection of personal information and preferences during\ninteractions, and as future multimodal models, such as GPT-4, may frequently\nrequire users to upload private photos.\n3.2 Future Directions\nIn forthcoming research, the development of models based on ChatGPT and\nGPT-4 may focus on addressing these limitations to enhance their practical\napplications.","page_label":"25","title":"","creator":"LaTeX with hyperref","trapped":"/False"}


created_at: 2026-06-08 08:17:00
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: d5e5de36-c9b5-4a91-b1e1-ef54fd0217b3
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{"page_label":"25","subject":"","keywords":"","page":24,"trapped":"/False","author":"","moddate":"2023-04-05T00:33:07+00:00","title":"","producer":"pdfTeX-1.40.21","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","creationdate":"2023-04-05T00:33:07+00:00","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","creator":"LaTeX with hyperref","total_pages":35,"chroma:document":"applications.\nPrimarily, researchers should continue to work on reﬁning model training\nmethodologies while ﬁltering pre-training data to minimize the presence of mis-\nleading information in the model’s knowledge base, thereby obtaining accurate\nresponses. Concurrently, it is crucial to emphasize training approaches that\neconomize computational resources, thereby mitigating costs and broadening\npotential application scenarios."}


created_at: 2026-06-08 08:17:00
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
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{"creator":"LaTeX with hyperref","chroma:document":"potential application scenarios.\nMoreover, the advancements in context-awareness and disambiguation tech-\nnologies are anticipated to facilitate enhanced comprehension of complex queries\n25","creationdate":"2023-04-05T00:33:07+00:00","producer":"pdfTeX-1.40.21","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","subject":"","page":24,"source":"./data/Summary of ChatGPTGPT-4 Research.pdf","moddate":"2023-04-05T00:33:07+00:00","title":"","author":"","page_label":"25","keywords":"","trapped":"/False","total_pages":35}


created_at: 2026-06-08 08:17:00
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: b7df3d61-9162-48c2-bde8-c41413a9b8e9
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{"moddate":"2023-04-05T00:33:07+00:00","creator":"LaTeX with hyperref","subject":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","producer":"pdfTeX-1.40.21","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","trapped":"/False","chroma:document":"by models, improving the accuracy, relevance, and context-awareness of AI-\ngenerated content. Integrating real-time data streams can also keep these mod-\nels in sync with current events and trends, enabling them to provide up-to-date\ninformation such as live traﬃc, weather, and stock updates.\nAdditionally, developers should engage in interdisciplinary collaboration with\nspecialists from diverse domains, including policy-making, jurisprudence, and","page":25,"creationdate":"2023-04-05T00:33:07+00:00","title":"","author":"","page_label":"26","total_pages":35,"keywords":""}


created_at: 2026-06-08 08:17:00
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: e56b7675-b661-4dfd-9a6b-2a838b92efed
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{"page_label":"26","moddate":"2023-04-05T00:33:07+00:00","chroma:document":"sociology, with the objective of formulating standard and ethical frameworks\nfor LLM development, deployment, and utilization, thereby alleviating poten-\ntial harmful consequences. In terms of public awareness and education, manda-\ntory awareness training should be implemented prior to large-scale public de-\nployment and application to increase public awareness of LLM capabilities and\nlimitations while promoting responsible and informed utilization, especially in","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","creationdate":"2023-04-05T00:33:07+00:00","page":25,"creator":"LaTeX with hyperref","author":"","trapped":"/False","subject":"","total_pages":35,"producer":"pdfTeX-1.40.21","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","title":"","keywords":""}


created_at: 2026-06-08 08:17:00
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: 007a44d9-fe4b-42ca-bb0c-37bd874f1478
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{"creator":"LaTeX with hyperref","total_pages":35,"title":"","chroma:document":"industries such as K-12 education and journalism.\nFinally, the inﬂuence of ChatGPT and GPT-4 should not be limited to\njust the NLP ﬁeld. They also show promising prospects in the areas of com-\nputer vision, brain-inspired AI, and robotics. These models exhibit a capacity\nfor learning and comprehension comparable with human-level intelligence, po-\nsitioning them as a pivotal component in the development of artiﬁcial general","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","trapped":"/False","keywords":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","page_label":"26","subject":"","author":"","creationdate":"2023-04-05T00:33:07+00:00","page":25,"producer":"pdfTeX-1.40.21","moddate":"2023-04-05T00:33:07+00:00"}


created_at: 2026-06-08 08:17:00
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: 10c84749-b3f6-4f8d-bf7d-7021022271e9
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{"page":25,"moddate":"2023-04-05T00:33:07+00:00","creator":"LaTeX with hyperref","trapped":"/False","keywords":"","page_label":"26","chroma:document":"intelligence (AGI)[101]. Their ability to facilitate seamless interactions between\nhumans and robots paves the way for the execution of more complex tasks.\nThe remarkable capacity of zero-shot in-context learning of these models en-\nables quick adaptation to new tasks without the requirement for labeled data\nfor ﬁne-tuning, which is a critical challenge in ﬁelds like medical informatics[102]\nand robotics[103] where the availability of labeled data is commonly limited or\nnon-existent.","producer":"pdfTeX-1.40.21","author":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","title":"","total_pages":35,"ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","subject":"","creationdate":"2023-04-05T00:33:07+00:00"}


created_at: 2026-06-08 08:17:00
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: c7a80c18-6294-46c6-a9bd-52e05859d53f
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{"moddate":"2023-04-05T00:33:07+00:00","title":"","page_label":"26","creator":"LaTeX with hyperref","page":25,"creationdate":"2023-04-05T00:33:07+00:00","keywords":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","trapped":"/False","producer":"pdfTeX-1.40.21","author":"","subject":"","total_pages":35,"chroma:document":"non-existent.\n4 Conclusion\nThis review paper provides a comprehensive survey of ChatGPT and GPT-4,\nhighlighting their potential applications and signiﬁcant contributions to the ﬁeld\nof natural language processing. The ﬁndings of this study reveal that the interest\nin these models is growing rapidly, and they have shown considerable potential\nfor application across a wide range of domains. One key factor contributing\nto the success of ChatGPT and GPT-4 is their ability to perform large-scale","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2"}


created_at: 2026-06-08 08:17:00
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: 095bf153-01c4-43de-bc25-2276a5ad18bc
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{"moddate":"2023-04-05T00:33:07+00:00","title":"","page":25,"creationdate":"2023-04-05T00:33:07+00:00","author":"","subject":"","creator":"LaTeX with hyperref","keywords":"","total_pages":35,"page_label":"26","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","chroma:document":"pre-training, which captures knowledge from the vast expanse of the internet,\nallowing the models to learn from a massive amount of data. The integration of\nReinforcement Learning from Human Feedback (RLHF) has further enhanced\nthe model’s adaptability and performance, making it highly eﬃcient in process-\ning natural language. This study has also identiﬁed several potential ethical\nconcerns related to the development and use of ChatGPT and GPT-4. For in-","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","trapped":"/False","producer":"pdfTeX-1.40.21"}


created_at: 2026-06-08 08:17:00
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: 93f0f9e1-f6c6-4233-89bf-72dd2b2c0397
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{"total_pages":35,"subject":"","trapped":"/False","author":"","creator":"LaTeX with hyperref","creationdate":"2023-04-05T00:33:07+00:00","moddate":"2023-04-05T00:33:07+00:00","keywords":"","page_label":"26","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","title":"","chroma:document":"stance, there are concerns about the generation of biased or harmful content,\nprivacy violations, and the potential for misuse of the technology. It is crucial\nto address these concerns and ensure that ChatGPT and GPT-4 are developed\nand used in a responsible and ethical manner. Furthermore, the results of this\nstudy demonstrate that there is signiﬁcant potential for ChatGPT and GPT-4\n26","producer":"pdfTeX-1.40.21","page":25,"source":"./data/Summary of ChatGPTGPT-4 Research.pdf"}


created_at: 2026-06-08 08:17:00
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: 95312a2a-af55-43cf-a5d0-b76c79c8a5cd
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{"total_pages":35,"title":"","moddate":"2023-04-05T00:33:07+00:00","author":"","creationdate":"2023-04-05T00:33:07+00:00","page":26,"creator":"LaTeX with hyperref","subject":"","keywords":"","chroma:document":"to be applied in a range of domains, including education, history, mathematics,\nphysics, and more. These models can facilitate tasks such as generating sum-\nmaries, answering questions, and providing personalized recommendations to\nusers. Overall, the insights presented in this review paper can serve as a useful\nguide for researchers and practitioners looking to advance the ﬁeld of natural\nlanguage processing. Future research in this ﬁeld should focus on addressing","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","producer":"pdfTeX-1.40.21","trapped":"/False","page_label":"27","source":"./data/Summary of ChatGPTGPT-4 Research.pdf"}


created_at: 2026-06-08 08:17:00
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: 5658207d-74ee-4a08-8352-d1efc9555b92
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{"chroma:document":"ethical concerns, exploring new applications, and ensuring the responsible use\nof ChatGPT and GPT-4. The potential of these models to revolutionize natural\nlanguage processing is enormous, and we look forward to seeing more develop-\nments in this ﬁeld.\nReferences\n[1] Radford A, Narasimhan K, Salimans T, Sutskever I, et al. Improving\nlanguage understanding by generative pre-training. OpenAI. 2018.\n[2] Radford A, Wu J, Child R, Luan D, Amodei D, Sutskever I, et al.","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","creationdate":"2023-04-05T00:33:07+00:00","subject":"","title":"","total_pages":35,"trapped":"/False","page":26,"moddate":"2023-04-05T00:33:07+00:00","keywords":"","author":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","page_label":"27","producer":"pdfTeX-1.40.21","creator":"LaTeX with hyperref"}


created_at: 2026-06-08 08:17:00
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: 6c200355-7a5b-4213-8973-0791924b185d
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created_at: 2026-06-08 08:17:00
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: e88ac3aa-6797-4c02-b986-cc0664932896
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{"moddate":"2023-04-05T00:33:07+00:00","author":"","producer":"pdfTeX-1.40.21","subject":"","creationdate":"2023-04-05T00:33:07+00:00","creator":"LaTeX with hyperref","page":26,"chroma:document":"processing systems. 2020;33:1877-901.\n[5] Ouyang L, Wu J, Jiang X, Almeida D, Wainwright CL, Mishkin P, et al.\nTraining language models to follow instructions with human feedback.\narXiv preprint arXiv:220302155. 2022.\n[6] Christiano PF, Leike J, Brown T, Martic M, Legg S, Amodei D. Deep\nreinforcement learning from human preferences. Advances in neural infor-\nmation processing systems. 2017;30.\n[7] Frieder S, Pinchetti L, Griﬃths RR, Salvatori T, Lukasiewicz T, Pe-","page_label":"27","title":"","trapped":"/False","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","keywords":"","total_pages":35}


created_at: 2026-06-08 08:17:00
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: 96008efc-7499-4a46-97db-04eef971b2c2
encoding: FLOAT32
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{"title":"","subject":"","trapped":"/False","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","total_pages":35,"creationdate":"2023-04-05T00:33:07+00:00","creator":"LaTeX with hyperref","keywords":"","producer":"pdfTeX-1.40.21","page":26,"page_label":"27","author":"","moddate":"2023-04-05T00:33:07+00:00","chroma:document":"tersen PC, et al. Mathematical capabilities of chatgpt. arXiv preprint\narXiv:230113867. 2023.\n[8] Pardos ZA, Bhandari S. Learning gain diﬀerences between ChatGPT and\nhuman tutor generated algebra hints. arXiv preprint arXiv:230206871.\n2023.\n[9] Shakarian P, Koyyalamudi A, Ngu N, Mareedu L. An Independent Eval-\nuation of ChatGPT on Mathematical Word Problems (MWP). arXiv\npreprint arXiv:230213814. 2023.\n27"}


created_at: 2026-06-08 08:17:00
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: b27ab3da-68ec-40ae-9089-0894482ceb8a
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created_at: 2026-06-08 08:17:00
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: 1777583b-a6f6-45c7-88cb-c1231a91f757
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created_at: 2026-06-08 08:17:00
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: 6cbe8c34-0822-436e-bd82-de88ca093db7
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{"subject":"","producer":"pdfTeX-1.40.21","chroma:document":"[17] Wang S, Scells H, Koopman B, Zuccon G. Can chatgpt write a good\nboolean query for systematic review literature search? arXiv preprint\narXiv:230203495. 2023.\n[18] Kuzman T, Mozetic I, Ljubeˇ sic N. ChatGPT: Beginning of an End of\nManual Linguistic Data Annotation? Use Case of Automatic Genre Iden-\ntiﬁcation. arXiv e-prints. 2023:arXiv-2303.\n[19] Amin MM, Cambria E, Schuller BW. Will Aﬀective Computing Emerge\nfrom Foundation Models and General AI? A First Evaluation on Chat-","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","author":"","title":"","total_pages":35,"page":27,"trapped":"/False","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","page_label":"28","keywords":"","creator":"LaTeX with hyperref","moddate":"2023-04-05T00:33:07+00:00","creationdate":"2023-04-05T00:33:07+00:00"}


created_at: 2026-06-08 08:17:00
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: 0f0d52fc-4677-4cc5-8118-f73d20bddb73
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created_at: 2026-06-08 08:17:00
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created_at: 2026-06-08 08:17:00
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created_at: 2026-06-08 08:17:00
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created_at: 2026-06-08 08:17:00
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created_at: 2026-06-08 08:17:00
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created_at: 2026-06-08 08:17:00
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created_at: 2026-06-08 08:18:55
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created_at: 2026-06-08 08:18:55
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created_at: 2026-06-08 08:18:55
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
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created_at: 2026-06-08 08:18:55
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: 01f19a5f-2cdb-4316-8c33-c96b6cc1fc32
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{"keywords":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","page_label":"1","producer":"pdfTeX-1.40.21","moddate":"2023-04-05T00:33:07+00:00","creator":"LaTeX with hyperref","trapped":"/False","page":0,"creationdate":"2023-04-05T00:33:07+00:00","chroma:document":"entire world wide web, instruction ﬁne-tuning and Reinforcement Learn-\ning from Human Feedback (RLHF) have played signiﬁcant roles in en-\nhancing LLMs’ adaptability and performance. We performed an in-depth\nanalysis of 194 relevant papers on arXiv, encompassing trend analysis,\nword cloud representation, and distribution analysis across various appli-\ncation domains. The ﬁndings reveal a signiﬁcant and increasing interest\nin ChatGPT/GPT-4 research, predominantly centered on direct natural","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","total_pages":35,"subject":"","author":"","title":""}


created_at: 2026-06-08 08:18:55
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
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created_at: 2026-06-08 08:18:55
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
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{"chroma:document":"medicine, and physics. This study endeavors to furnish insights into Chat-\nGPT’s capabilities, potential implications, ethical concerns, and oﬀer di-\nrection for future advancements in this ﬁeld.\n1 Introduction\nRecent advances in natural language processing (NLP) have led to the devel-\nopment of powerful language models such as the GPT (Generative Pre-trained\nTransformer) series [1, 2, 3, 4, 5], including large language models (LLM) such","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","author":"","page_label":"2","producer":"pdfTeX-1.40.21","page":1,"keywords":"","moddate":"2023-04-05T00:33:07+00:00","trapped":"/False","total_pages":35,"ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","creationdate":"2023-04-05T00:33:07+00:00","subject":"","creator":"LaTeX with hyperref","title":""}


created_at: 2026-06-08 08:18:55
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
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created_at: 2026-06-08 08:18:55
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
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{"subject":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","chroma:document":"pervised pre-training. ChatGPT is a successor to InstructGPT. Since its release\nin December 2022, ChatGPT has been equipped with these advanced develop-\nments, leading to impressive performances in various downstream NLP tasks\nsuch as reasoning and generalized text generation. These unprecedented NLP\ncapabilities spur applications in diverse domains such as education, healthcare,\nhuman-machine interaction, medicine and scientiﬁc research. ChatGPT has re-","page":1,"creator":"LaTeX with hyperref","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","total_pages":35,"moddate":"2023-04-05T00:33:07+00:00","trapped":"/False","author":"","page_label":"2","producer":"pdfTeX-1.40.21","creationdate":"2023-04-05T00:33:07+00:00","keywords":"","title":""}


created_at: 2026-06-08 08:18:55
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: 84aa280a-32ed-4663-ac7b-072ccd4960b0
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{"author":"","creator":"LaTeX with hyperref","trapped":"/False","creationdate":"2023-04-05T00:33:07+00:00","keywords":"","moddate":"2023-04-05T00:33:07+00:00","title":"","producer":"pdfTeX-1.40.21","chroma:document":"ceived widespread attention and interest, leading to an increasing number of\napplications and research that harness its exceeding potential. The open release\nof the multi-modal GPT-4 model further expands the horizon of large language\nmodels and empowers exciting developments that involve diverse data beyond\ntext.\nThe purpose of this paper is to provide a comprehensive survey of the existing\nresearch on ChatGPT and its potential applications in various ﬁelds. To achieve","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","total_pages":35,"subject":"","page_label":"2","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","page":1}


created_at: 2026-06-08 08:18:55
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: 62443999-d9c6-4693-a1fd-3fb1e422169a
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{"moddate":"2023-04-05T00:33:07+00:00","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","page_label":"2","producer":"pdfTeX-1.40.21","title":"","total_pages":35,"subject":"","trapped":"/False","creator":"LaTeX with hyperref","chroma:document":"this goal, we conducted a thorough analysis of papers related to ChatGPT in\nthe arXiv repository. As of April 1st, 2023, there are a total of 194 papers\nmentioning ChatGPT on arXiv. In this study, we conducted a trend analysis of\nthese papers and generated a word cloud to visualize the commonly used terms.\nAdditionally, we also examined the distribution of the papers across various\nﬁelds and presented the corresponding statistics. Figure 1 displays the daily","page":1,"author":"","creationdate":"2023-04-05T00:33:07+00:00","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","keywords":""}


created_at: 2026-06-08 08:18:55
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: d4b47b7d-847e-4363-96d1-697f6ebd9fe0
encoding: FLOAT32
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{"creator":"LaTeX with hyperref","author":"","title":"","chroma:document":"submission trend of papers related to ChatGPT, indicating a growing interest\nin this ﬁeld. Figure 2 illustrates the word cloud analysis of all the papers. We\ncan observe that the current research is primarily focused on natural language\nprocessing, but there is still signiﬁcant potential for research in other ﬁelds such\n2","trapped":"/False","producer":"pdfTeX-1.40.21","subject":"","keywords":"","page_label":"2","moddate":"2023-04-05T00:33:07+00:00","creationdate":"2023-04-05T00:33:07+00:00","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","total_pages":35,"page":1}


created_at: 2026-06-08 08:18:55
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
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{"subject":"","moddate":"2023-04-05T00:33:07+00:00","producer":"pdfTeX-1.40.21","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","creationdate":"2023-04-05T00:33:07+00:00","trapped":"/False","total_pages":35,"creator":"LaTeX with hyperref","page_label":"3","keywords":"","page":2,"ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","author":"","chroma:document":"Figure 1: The number of papers submitted by researchers per day.\n3","title":""}


created_at: 2026-06-08 08:18:55
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: 31254705-e9dc-40ed-8759-ac1990d38514
encoding: FLOAT32
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{"page":3,"moddate":"2023-04-05T00:33:07+00:00","total_pages":35,"keywords":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","trapped":"/False","title":"","page_label":"4","creator":"LaTeX with hyperref","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","author":"","chroma:document":"Figure 2: Word cloud analysis of all the 194 papers.\nas education and history. This is further supported by Figure 3, which displays\nthe distribution of submitted papers across various ﬁelds, highlighting the need\nfor more research and development in these areas.\nThis paper aims to shed light on the promising capabilities of ChatGPT\nand provide insight into its potential impact in the future, including ethical\nconsiderations. Through this survey, we hope to provide insights into how these","creationdate":"2023-04-05T00:33:07+00:00","producer":"pdfTeX-1.40.21","subject":""}


created_at: 2026-06-08 08:18:55
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: 4e171d05-ec86-4ec5-ae01-dff50f67142a
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{"title":"","creationdate":"2023-04-05T00:33:07+00:00","author":"","trapped":"/False","chroma:document":"models can be improved and extended in the future. In section 2, we will\nreview the existing work related to ChatGPT, including its applications, ethical\nconsiderations, and evaluation. In addition to discussing the current state of\nresearch related to ChatGPT, we will also explore its limitations in section 3.\nFurthermore, we will provide guidance on future directions for language model\ndevelopment.\n2 Related work of ChatGPT","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","keywords":"","moddate":"2023-04-05T00:33:07+00:00","total_pages":35,"page":3,"producer":"pdfTeX-1.40.21","creator":"LaTeX with hyperref","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","page_label":"4","subject":""}


created_at: 2026-06-08 08:18:55
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: 7e61b6de-7e0c-47bd-b351-2868ec761e59
encoding: FLOAT32
metadata:
{"creationdate":"2023-04-05T00:33:07+00:00","moddate":"2023-04-05T00:33:07+00:00","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","keywords":"","title":"","page":3,"producer":"pdfTeX-1.40.21","subject":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","creator":"LaTeX with hyperref","total_pages":35,"chroma:document":"development.\n2 Related work of ChatGPT\nIn this section, we review the latest research related to the application, ethics,\nand evaluation of ChatGPT.\n2.1 Application of ChatGPT\n2.1.1 Question And Answering\nIn the education ﬁeld\nChatGPT is commonly used for question and answers testing in the edu-\ncation sector. Users can use ChatGPT to learn, compare and verify answers\nfor diﬀerent academic subjects such as physics, mathematics, and chemistry,\n4","trapped":"/False","author":"","page_label":"4"}


created_at: 2026-06-08 08:18:55
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
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{"creator":"LaTeX with hyperref","total_pages":35,"creationdate":"2023-04-05T00:33:07+00:00","keywords":"","page":4,"page_label":"5","subject":"","producer":"pdfTeX-1.40.21","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","author":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","moddate":"2023-04-05T00:33:07+00:00","trapped":"/False","chroma:document":"Figure 3: The distribution of submitted papers across various ﬁelds.\n5","title":""}


created_at: 2026-06-08 08:18:55
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: e8f6a2df-c93d-4746-ad34-ff7dff8454e6
encoding: FLOAT32
metadata:
{"keywords":"","trapped":"/False","producer":"pdfTeX-1.40.21","moddate":"2023-04-05T00:33:07+00:00","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","subject":"","creator":"LaTeX with hyperref","page":5,"author":"","page_label":"6","chroma:document":"and/or conceptual subjects such as philosophy and religion. Additionally, users\ncan ask open-ended and analytical questions to understand the capabilities of\nChatGPT.\nIn the ﬁeld of mathematics, Frieder et al. [7] constructed the GHOSTS nat-\nural language dataset, which consists of graduate-level math test questions.\nThe authors tested ChatGPT’s math abilities on the GHOSTS dataset us-\ning a question-and-answer format and evaluated it according to ﬁne-grained","title":"","creationdate":"2023-04-05T00:33:07+00:00","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","total_pages":35}


created_at: 2026-06-08 08:18:55
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: fd4b5f1f-c25b-4c0e-aaef-75c4aba1a223
encoding: FLOAT32
metadata:
{"ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","producer":"pdfTeX-1.40.21","page":5,"keywords":"","page_label":"6","creationdate":"2023-04-05T00:33:07+00:00","title":"","total_pages":35,"subject":"","moddate":"2023-04-05T00:33:07+00:00","trapped":"/False","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","author":"","creator":"LaTeX with hyperref","chroma:document":"standards.In the Grad Text dataset, which covers simple set theory and logic\nproblems, ChatGPT performed the best. However, in the Olympiad-Problem-\nSolving dataset, ChatGPT performed poorly, receiving only two 4-point scores\n(out of a total of 5), with the majority of scores being 2 points. In the Holes-\nin-Proofs dataset, ChatGPT received the lowest score of 1 point. In the MATH\ndataset, ChatGPT only scored impressively in 26% of cases. These results sug-"}


created_at: 2026-06-08 08:18:55
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: b1cffc6f-7d7e-4bfe-babc-13f8869b84d7
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{"keywords":"","moddate":"2023-04-05T00:33:07+00:00","author":"","creationdate":"2023-04-05T00:33:07+00:00","chroma:document":"gest that ChatGPT’s math abilities are clearly lower than those of ordinary\nmath graduate students. Although ChatGPT can generally understand math\nproblems, it fails to provide the correct solutions. Pardos et al. [8] used the\nOpen Adaptive Tutoring system (OATutor) to investigate whether prompts gen-\nerated by ChatGPT were helpful for learning algebra, with 77 participants from\nMechanical Turk taking part in the experiment. The experiment used ques-","total_pages":35,"producer":"pdfTeX-1.40.21","title":"","subject":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","page":5,"ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","trapped":"/False","creator":"LaTeX with hyperref","page_label":"6"}


created_at: 2026-06-08 08:18:55
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: fb4d8af3-e314-4b44-8a8b-55ecb1d859ad
encoding: FLOAT32
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{"title":"","page":5,"chroma:document":"tions from OpenStax’s Elementary and Intermediate Algebra textbooks. These\nparticipants were randomly assigned to either a control group (with manual\nprompts) or an experimental group (with ChatGPT prompts). For each ques-\ntion in both courses, the authors obtained answers from ChatGPT through a\nquestion-and-answer format and evaluated scores according to three criteria:\nChatGPT provided an answer, the answer was correct, and inappropriate lan-","moddate":"2023-04-05T00:33:07+00:00","creationdate":"2023-04-05T00:33:07+00:00","total_pages":35,"trapped":"/False","page_label":"6","keywords":"","creator":"LaTeX with hyperref","subject":"","producer":"pdfTeX-1.40.21","author":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2"}


created_at: 2026-06-08 08:18:55
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: 4ec7f73a-3a2e-4b3a-8f44-a5e9e8b77484
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{"author":"","total_pages":35,"creationdate":"2023-04-05T00:33:07+00:00","page":5,"trapped":"/False","producer":"pdfTeX-1.40.21","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","title":"","subject":"","moddate":"2023-04-05T00:33:07+00:00","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","creator":"LaTeX with hyperref","keywords":"","chroma:document":"guage was not used in the answer. The study found that 70% of prompts gener-\nated by ChatGPT passed manual quality checks, and both humans and Chat-\nGPT produced positive learning gains. However, the scores of human prompts\nranged from 74.59% to 84.32%, signiﬁcantly higher than those of ChatGPT\nprompts. Shakarian et al. [9] studied the performance of ChatGPT on math\nword problems (MWPs), using the DRAW-1K dataset for experimentation. The","page_label":"6"}


created_at: 2026-06-08 08:18:55
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: 7b3ce37c-1c49-4f1e-9842-e5da6a1b9dc4
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metadata:
{"author":"","page":5,"creator":"LaTeX with hyperref","producer":"pdfTeX-1.40.21","total_pages":35,"moddate":"2023-04-05T00:33:07+00:00","keywords":"","page_label":"6","subject":"","creationdate":"2023-04-05T00:33:07+00:00","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","trapped":"/False","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","title":"","chroma:document":"dataset consists of 1000 MWPs and their answers, along with algebraic equa-\ntion templates for solving such problems. The authors used the idea of machine\nlearning introspection and built performance prediction models using random\nforests and XGBoost, and evaluated them on the dataset using ﬁve-fold cross-\nvalidation. ChatGPT’s accuracy increased from an initial 34% to a ﬁnal 69%,\nwhile its recall increased from an initial 41% to a ﬁnal 83%. The authors also"}


created_at: 2026-06-08 08:18:55
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: f2c9717e-e03f-454f-ae7f-1a5f2cbdc961
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metadata:
{"moddate":"2023-04-05T00:33:07+00:00","title":"","producer":"pdfTeX-1.40.21","page_label":"6","page":5,"source":"./data/Summary of ChatGPTGPT-4 Research.pdf","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","trapped":"/False","total_pages":35,"keywords":"","creator":"LaTeX with hyperref","author":"","subject":"","chroma:document":"found that ChatGPT’s failure rate decreased from an initial 84% to a ﬁnal\n20%, indicating that performance can vary greatly depending on speciﬁc job\nrequirements.\nIn the ﬁeld of physics, Lehnert et al. [10] explored the capabilities and limita-\ntions of ChatGPT by studying how it handles obscure physics topics such as the\nswamp land conjecture in string theory. The experimental dialogue began with\nbroader and more general questions in the ﬁeld of string theory before narrowing","creationdate":"2023-04-05T00:33:07+00:00"}


created_at: 2026-06-08 08:18:55
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: a12276fa-aa52-4095-b380-e8879245e83a
encoding: FLOAT32
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{"creator":"LaTeX with hyperref","page_label":"6","author":"","page":5,"source":"./data/Summary of ChatGPTGPT-4 Research.pdf","total_pages":35,"trapped":"/False","keywords":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","creationdate":"2023-04-05T00:33:07+00:00","title":"","subject":"","producer":"pdfTeX-1.40.21","chroma:document":"down to speciﬁc swamp land conjectures and examining ChatGPT’s understand-\ning of them. The study found that ChatGPT could deﬁne and explain diﬀerent\n6","moddate":"2023-04-05T00:33:07+00:00"}


created_at: 2026-06-08 08:18:55
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: 0dfd0d0f-2352-4430-97e2-1bfbad1fb0bc
encoding: FLOAT32
metadata:
{"moddate":"2023-04-05T00:33:07+00:00","producer":"pdfTeX-1.40.21","title":"","page_label":"7","keywords":"","creationdate":"2023-04-05T00:33:07+00:00","creator":"LaTeX with hyperref","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","subject":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","trapped":"/False","chroma:document":"concepts in various styles, but was not eﬀective in truly connecting various con-\ncepts. It would conﬁdently provide false information and fabricate statements\nwhen necessary, indicating that ChatGPT cannot truly create new knowledge\nor establish new connections. However, in terms of identifying analogies and\ndescribing abstract concepts of visual representation, ChatGPT can cleverly\nuse language. Kortemeyer et al. [11] evaluated ChatGPT’s ability to answer","total_pages":35,"author":"","page":6}


created_at: 2026-06-08 08:18:55
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: 40d4c6f3-ca01-47fb-9859-976586d72ba7
encoding: FLOAT32
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{"creator":"LaTeX with hyperref","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","total_pages":35,"ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","chroma:document":"calculus-based physics questions through a question-and-answer test. The tests\nincluded online homework, clicker questions, programming exercises, and exams\ncovering classical mechanics, thermodynamics, electricity and magnetism, and\nmodern physics. While ChatGPT was able to pass the course, it also demon-\nstrated many misconceptions and errors commonly held by beginners. West et\nal. [12] used the Force Concept Inventory (FCI) to evaluate ChatGPT’s accu-","moddate":"2023-04-05T00:33:07+00:00","page_label":"7","subject":"","producer":"pdfTeX-1.40.21","author":"","trapped":"/False","keywords":"","page":6,"title":"","creationdate":"2023-04-05T00:33:07+00:00"}


created_at: 2026-06-08 08:18:55
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: 1e466ebe-0119-4fb5-bf8f-d2d596234c7b
encoding: FLOAT32
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{"title":"","moddate":"2023-04-05T00:33:07+00:00","page":6,"producer":"pdfTeX-1.40.21","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","creationdate":"2023-04-05T00:33:07+00:00","trapped":"/False","keywords":"","author":"","creator":"LaTeX with hyperref","subject":"","page_label":"7","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","chroma:document":"racy in answering physics concept problems related to kinematics and Newtonian\nmechanics in the ﬁrst semester of college physics. The FCI covers topics such\nas kinematics, projectile motion, free fall, circular motion, and Newton’s laws.\nThe study included data from 415 students who took the FCI at the end of the\nsemester, with an average score of 56%, while ChatGPT scored approximately\nbetween 50% to 65%. The authors demonstrated that ChatGPT’s performance","total_pages":35}


created_at: 2026-06-08 08:18:55
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: 3bc141bd-d077-4b04-897b-9a7336b5e7c8
encoding: FLOAT32
metadata:
{"creator":"LaTeX with hyperref","producer":"pdfTeX-1.40.21","page_label":"7","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","trapped":"/False","chroma:document":"in physics learning can reach or even exceed the average level of a semester of\ncollege physics.\nIn the medical ﬁeld\nChatGPT’s question-answering capabilities can also be applied in the med-\nical ﬁeld, such as for answering medical questions from patients or assisting\nhealthcare professionals in diagnosing diseases. Nov et al. [13] evaluated the fea-\nsibility of using ChatGPT for patient-doctor communication. The experiment","total_pages":35,"author":"","page":6,"moddate":"2023-04-05T00:33:07+00:00","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","subject":"","keywords":"","creationdate":"2023-04-05T00:33:07+00:00","title":""}


created_at: 2026-06-08 08:18:55
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: 10ce2b8e-ad2b-4c2b-93c6-02829cc79586
encoding: FLOAT32
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{"subject":"","creationdate":"2023-04-05T00:33:07+00:00","page":6,"keywords":"","total_pages":35,"producer":"pdfTeX-1.40.21","chroma:document":"extracted 10 representative patient-doctor interactions from EHR, placed the\npatient’s questions in ChatGPT, and asked ChatGPT to respond using roughly\nthe same number of words as the doctor’s response. Each patient’s question was\nanswered by either the doctor or ChatGPT, and the patient was informed that\n5 were answered by the doctor and 5 were generated by ChatGPT, and was\nasked to correctly identify the source of the response. The results of the exper-","trapped":"/False","author":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","creator":"LaTeX with hyperref","title":"","page_label":"7","moddate":"2023-04-05T00:33:07+00:00"}


created_at: 2026-06-08 08:18:55
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: 2050c778-e955-4a2f-851a-0d22f64356bb
encoding: FLOAT32
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{"subject":"","page":6,"keywords":"","trapped":"/False","creationdate":"2023-04-05T00:33:07+00:00","total_pages":35,"author":"","title":"","creator":"LaTeX with hyperref","page_label":"7","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","producer":"pdfTeX-1.40.21","chroma:document":"iment showed that the probability of correctly identifying ChatGPT’s response\nwas 65.5%, while the probability of correctly identifying the doctor’s response\nwas 65.1%. In addition, the experiment found that the patient’s response to\nthe trustworthiness of ChatGPT’s function was weakly positive (average Likert\nscore: 3.4), and trust decreased as the complexity of health-related tasks in\nthe questions increased. ChatGPT’s responses to patient questions were only","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","moddate":"2023-04-05T00:33:07+00:00"}


created_at: 2026-06-08 08:18:55
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: 35a75a68-9053-4e41-a4bf-a8eeef48a706
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{"page":6,"trapped":"/False","author":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","moddate":"2023-04-05T00:33:07+00:00","creationdate":"2023-04-05T00:33:07+00:00","chroma:document":"slightly diﬀerent from those of doctors, but people seem to trust ChatGPT to\nanswer low-risk health questions, while for complex medical questions, people\nstill tend to trust the doctor’s responses and advice.\nTu et al. [14] explored the causal discovery ability of ChatGPT in the diag-\nnosis of neuropathic pain. Causal relationship discovery aims to reveal potential\nunknown causal relationships based purely on observed data [15]. The experi-","creator":"LaTeX with hyperref","page_label":"7","keywords":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","total_pages":35,"subject":"","title":"","producer":"pdfTeX-1.40.21"}


created_at: 2026-06-08 08:18:55
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
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{"moddate":"2023-04-05T00:33:07+00:00","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","page":6,"producer":"pdfTeX-1.40.21","trapped":"/False","creator":"LaTeX with hyperref","chroma:document":"mental results found that ChatGPT has some limitations in understanding new\n7","creationdate":"2023-04-05T00:33:07+00:00","title":"","keywords":"","total_pages":35,"page_label":"7","subject":"","author":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2"}


created_at: 2026-06-08 08:18:55
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: 9c8c2114-74a3-4e11-a3e1-3eaba3b35e14
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{"ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","title":"","creator":"LaTeX with hyperref","subject":"","page":7,"moddate":"2023-04-05T00:33:07+00:00","creationdate":"2023-04-05T00:33:07+00:00","chroma:document":"knowledge and concepts beyond the existing textual training data corpus, that\nis, it only understands language commonly used to describe situations and not\nunderlying knowledge. In addition, its performance consistency and stability are\nnot high, as the experiment observed that it would provide diﬀerent answers for\nthe same question under multiple inquiries. However, despite the many limita-\ntions of ChatGPT, we believe that it has a great opportunity to improve causal\nrelationship research.","total_pages":35,"page_label":"8","author":"","trapped":"/False","producer":"pdfTeX-1.40.21","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","keywords":""}


created_at: 2026-06-08 08:18:55
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: 04e9d7f2-b4e5-411f-b63b-2583951f1393
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{"title":"","creationdate":"2023-04-05T00:33:07+00:00","moddate":"2023-04-05T00:33:07+00:00","keywords":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","subject":"","producer":"pdfTeX-1.40.21","chroma:document":"relationship research.\nIn the other ﬁeld\nGuo et al. [16] attempted to apply ChatGPT in the ﬁeld of communication,\nspeciﬁcally using ChatGPT for ordered importance semantic communication,\nwhere ChatGPT plays the role of an intelligent consulting assistant that can\nreplace humans in identifying the semantic importance of words in messages and\ncan be directly embedded into the current communication system. For a message\nto be transmitted, the sender ﬁrst utilizes ChatGPT to output the semantic","creator":"LaTeX with hyperref","page":7,"page_label":"8","trapped":"/False","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","author":"","total_pages":35}


created_at: 2026-06-08 08:18:55
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: 298a25f2-5da4-4156-805c-c0010fe1e02e
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{"creationdate":"2023-04-05T00:33:07+00:00","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","title":"","keywords":"","page_label":"8","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","author":"","page":7,"trapped":"/False","moddate":"2023-04-05T00:33:07+00:00","producer":"pdfTeX-1.40.21","creator":"LaTeX with hyperref","total_pages":35,"subject":"","chroma:document":"importance order of each word. Then, the transmitter executes an unequal error\nprotection transmission strategy based on the importance order to make the\ntransmission of important words in the message more reliable. The experimental\nresults show that the error rate and semantic loss of important words measured\nin the communication system embedded with ChatGPT are much lower than\nthose of existing communication schemes, indicating that ChatGPT can protect"}


created_at: 2026-06-08 08:18:55
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: 1dc85f1b-5030-4fcf-93fb-9d3ee10bf692
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{"source":"./data/Summary of ChatGPTGPT-4 Research.pdf","page_label":"8","creator":"LaTeX with hyperref","author":"","trapped":"/False","moddate":"2023-04-05T00:33:07+00:00","chroma:document":"important words well and make semantic communication more reliable.\nWang et al. [17] studied the eﬀectiveness of ChatGPT in generating high-\nquality Boolean queries for systematic literature search. They designed a wide\nrange of prompts and investigated these tasks on more than 100 systematic\nreview topics. In the end, queries generated by ChatGPT achieved higher accu-\nracy compared to the currently most advanced query generation methods but","creationdate":"2023-04-05T00:33:07+00:00","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","total_pages":35,"keywords":"","title":"","subject":"","page":7,"producer":"pdfTeX-1.40.21"}


created_at: 2026-06-08 08:18:55
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: 6c006023-fe6d-4884-a9dc-04ee6bc4faac
encoding: FLOAT32
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{"source":"./data/Summary of ChatGPTGPT-4 Research.pdf","trapped":"/False","producer":"pdfTeX-1.40.21","subject":"","creationdate":"2023-04-05T00:33:07+00:00","total_pages":35,"creator":"LaTeX with hyperref","title":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","chroma:document":"at the cost of reduced recall. For time-limited rapid reviews, it is often accept-\nable to trade oﬀ higher precision for lower recall. Additionally, ChatGPT can\ngenerate high search accuracy Boolean queries by guiding the prompts. How-\never, it should be noted that when two queries use the same prompts, ChatGPT\ngenerates diﬀerent queries, indicating its limitations in consistency and stabil-\nity. Overall, this study demonstrated the potential of ChatGPT in generating","page":7,"keywords":"","author":"","page_label":"8","moddate":"2023-04-05T00:33:07+00:00"}


created_at: 2026-06-08 08:18:55
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: 4b08ce1f-4d8b-481d-ad50-39386d2049ce
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{"moddate":"2023-04-05T00:33:07+00:00","total_pages":35,"keywords":"","chroma:document":"eﬀective Boolean queries for systematic literature searches.\n2.1.2 Text Classiﬁcation\nThe purpose of text classiﬁcation is to assign text data to predeﬁned categories.\nThis task is critical for many applications, including sentiment analysis, spam\ndetection, and topic modeling. While traditional machine learning algorithms\nhave been widely used for text classiﬁcation, recent advances in natural lan-\nguage processing have led to the development of more advanced techniques.","page_label":"8","trapped":"/False","page":7,"title":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","producer":"pdfTeX-1.40.21","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","creator":"LaTeX with hyperref","author":"","subject":"","creationdate":"2023-04-05T00:33:07+00:00"}


created_at: 2026-06-08 08:18:55
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: 79eaa25e-c26f-4fcd-af88-7f8a886fd761
encoding: FLOAT32
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{"creator":"LaTeX with hyperref","producer":"pdfTeX-1.40.21","subject":"","page_label":"8","title":"","creationdate":"2023-04-05T00:33:07+00:00","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","total_pages":35,"keywords":"","chroma:document":"ChatGPT has shown immense potential in this ﬁeld. Its ability to accurately\nclassify text, ﬂexibility in handling various classiﬁcation tasks, and potential for\ncustomization make it a valuable tool for text classiﬁcation, as evidenced by\nseveral studies in the literature.\n8","author":"","trapped":"/False","page":7,"source":"./data/Summary of ChatGPTGPT-4 Research.pdf","moddate":"2023-04-05T00:33:07+00:00"}


created_at: 2026-06-08 08:18:55
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: 2723a295-1686-4537-ac12-4766d5fc2699
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{"total_pages":35,"trapped":"/False","page_label":"9","page":8,"title":"","creationdate":"2023-04-05T00:33:07+00:00","keywords":"","chroma:document":"Kuzman et al. [18] employed ChatGPT for automatic genre recognition, with\nthe goal of simplifying the text classiﬁcation task by utilizing ChatGPT’s zero-\nshot classiﬁcation capability. They compared ChatGPT’s genre recognition per-\nformance, using two prompt languages (EN and SL), with the X-GENRE clas-\nsiﬁer based on the multilingual model XLM-RoBERTa on the English dataset\nEN-GINCO and the Slovenian dataset GINCO. The results showed that when","subject":"","creator":"LaTeX with hyperref","producer":"pdfTeX-1.40.21","moddate":"2023-04-05T00:33:07+00:00","author":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2"}


created_at: 2026-06-08 08:18:55
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: 055ab7e3-44f4-4f2c-ae2e-fcbfa2ee96f7
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{"source":"./data/Summary of ChatGPTGPT-4 Research.pdf","trapped":"/False","page":8,"title":"","page_label":"9","creator":"LaTeX with hyperref","producer":"pdfTeX-1.40.21","total_pages":35,"moddate":"2023-04-05T00:33:07+00:00","creationdate":"2023-04-05T00:33:07+00:00","author":"","subject":"","keywords":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","chroma:document":"EN was used as the prompt language, ChatGPT achieved Micro F1, Macro F1,\nand Accuracy scores of 0.74, 0.66, and 0.72. However, on the GINCO dataset,\nChatGPT’s genre recognition performance with both EN and SL prompt lan-\nguages was lower than that of the X-GENRE classiﬁer to varying degrees.\nAmin et al. [19] evaluated the text classiﬁcation ability of ChatGPT in\naﬀective computing by using it to perform personality prediction, sentiment"}


created_at: 2026-06-08 08:18:55
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: 999a2ba9-841a-40d6-aaf7-4fceaa3d8ede
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{"source":"./data/Summary of ChatGPTGPT-4 Research.pdf","keywords":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","total_pages":35,"creationdate":"2023-04-05T00:33:07+00:00","moddate":"2023-04-05T00:33:07+00:00","trapped":"/False","title":"","subject":"","author":"","chroma:document":"analysis, and suicide ideation detection tasks. They prompted ChatGPT with\ncorresponding prompts on three datasets: First Impressions, Sentiment140, and\nSuicide and Depression, and compared its classiﬁcation performance with three\nbaseline models: RoBERTa-base, Word2Vec, and BoW. The results showed\nthat ChatGPT’s accuracy and UAR for the ﬁve personality classiﬁcations on\nthe First Impressions dataset were lower than the baseline methods to varying","page":8,"page_label":"9","producer":"pdfTeX-1.40.21","creator":"LaTeX with hyperref"}


created_at: 2026-06-08 08:18:55
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: 418571d6-f006-49bb-abad-9d16788c64be
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{"page_label":"9","producer":"pdfTeX-1.40.21","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","creationdate":"2023-04-05T00:33:07+00:00","creator":"LaTeX with hyperref","author":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","subject":"","moddate":"2023-04-05T00:33:07+00:00","keywords":"","total_pages":35,"chroma:document":"degrees. On the Sentiment140 dataset, ChatGPT’s accuracy and UAR were 85.5\nand 85.5, respectively, which were better than the three baseline methods. On\nthe Suicide and Depression dataset, ChatGPT’s accuracy and UAR were 92.7\nand 91.2, respectively, which were lower than RoBERTa, the best-performing\nbaseline method.\nZhang et al. [20] employed ChatGPT for stance detection, which includes\nsupport and opposition. They used ChatGPT to classify the political stance","page":8,"trapped":"/False","title":""}


created_at: 2026-06-08 08:18:55
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: f606d289-004b-4e8c-b6ee-bdad7cb9a3a5
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{"creationdate":"2023-04-05T00:33:07+00:00","title":"","moddate":"2023-04-05T00:33:07+00:00","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","producer":"pdfTeX-1.40.21","author":"","page":8,"source":"./data/Summary of ChatGPTGPT-4 Research.pdf","creator":"LaTeX with hyperref","page_label":"9","subject":"","total_pages":35,"chroma:document":"of tweets in the SemEval-2016 and P-Stance datasets. SemEval-2016 contains\n4,870 English tweets, and they selected tweets with the most commonly occur-\nring FM, LA, and HC political labels for stance classiﬁcation. The P-Stance\ndataset has 21,574 English tweets, and they classiﬁed the stance of tweets to-\nwards Trump, Biden, and Bernie. The ﬁnal results showed that on the SemEval-\n2016 dataset, ChatGPT achieved F1-m scores of 68.4, 58.2, and 79.5 for the FM,","trapped":"/False","keywords":""}


created_at: 2026-06-08 08:18:55
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: 8dfae018-e859-43f1-9400-8fda72917f42
encoding: FLOAT32
metadata:
{"moddate":"2023-04-05T00:33:07+00:00","page":8,"author":"","total_pages":35,"page_label":"9","keywords":"","producer":"pdfTeX-1.40.21","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","subject":"","title":"","trapped":"/False","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","chroma:document":"LA, and HC political labels, and F1-avg scores of 72.6, 59.3, and 78.0, respec-\ntively. On the P-Stance dataset, ChatGPT achieved F1-m scores of 82.8, 82.3,\nand 79.4 for the Trump, Biden, and Bernie political ﬁgures, and F1-avg scores\nof 83.2, 82.0, and 79.4, respectively.\nHuang et al. [21] used ChatGPT to detect implicit hate speech in tweets.\nThey selected 12.5% (795 tweets) of the LatentHatred dataset containing im-\nplicit hate speech and asked ChatGPT to classify them into three categories:","creator":"LaTeX with hyperref","creationdate":"2023-04-05T00:33:07+00:00"}


created_at: 2026-06-08 08:18:55
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: 12460e9f-3814-48bc-9132-96a9ddbd5227
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{"chroma:document":"implicit hate speech, non-hate speech, and uncertain. The results showed that\nChatGPT correctly recognized 636 (80%) of the tweets. The number of tweets\nclassiﬁed as non-hate speech and uncertain were 146 (18.4%) and 13 (1.6%),\nrespectively. The results of the reclassiﬁcation of tweets in the non-hate speech\nand uncertain categories by Amazon Mechanical Turk (Mturk) workers were\nconsistent with ChatGPT’s classiﬁcation.\nOverall, ChatGPT has tremendous potential in text classiﬁcation tasks, as it","author":"","creator":"LaTeX with hyperref","title":"","page_label":"9","subject":"","creationdate":"2023-04-05T00:33:07+00:00","trapped":"/False","page":8,"total_pages":35,"ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","keywords":"","moddate":"2023-04-05T00:33:07+00:00","producer":"pdfTeX-1.40.21"}


created_at: 2026-06-08 08:18:55
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: 2bc059cb-9668-462d-b6d8-f4c1c28c1d83
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{"producer":"pdfTeX-1.40.21","author":"","creator":"LaTeX with hyperref","moddate":"2023-04-05T00:33:07+00:00","page_label":"9","page":8,"trapped":"/False","creationdate":"2023-04-05T00:33:07+00:00","total_pages":35,"subject":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","keywords":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","title":"","chroma:document":"can eﬀectively address problems such as genre identiﬁcation, sentiment analysis,\n9"}


created_at: 2026-06-08 08:18:55
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: babb5d9e-058e-43e7-95bf-e32f6e4ac67f
encoding: FLOAT32
metadata:
{"moddate":"2023-04-05T00:33:07+00:00","trapped":"/False","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","subject":"","page":9,"chroma:document":"stance detection, and more. However, there are still challenges that ChatGPT\nfaces in the ﬁeld of text classiﬁcation. Firstly, it struggles to perform well in\nclassiﬁcation tasks with rare or out-of-vocabulary words since it heavily relies\non the distribution of training data. Additionally, the signiﬁcant computational\nresources required for training and utilizing ChatGPT can limit its use in some\napplications.\n2.1.3 Text generation","author":"","creationdate":"2023-04-05T00:33:07+00:00","keywords":"","title":"","producer":"pdfTeX-1.40.21","creator":"LaTeX with hyperref","page_label":"10","total_pages":35}


created_at: 2026-06-08 08:18:55
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: 6068282b-7ec4-4559-921e-16254a0ecd77
encoding: FLOAT32
metadata:
{"page":9,"total_pages":35,"author":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","subject":"","keywords":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","chroma:document":"applications.\n2.1.3 Text generation\nWe live in an era of information explosion, and text is an eﬃcient way of trans-\nmitting information. The diversity of information has led to a diversity of text\ncategories. When researchers use ChatGPT’s text generation capabilities for\nresearch, they inevitably choose to generate diﬀerent types of text. In the pro-\ncess of reading papers, we found that the word count of the text generated by","creator":"LaTeX with hyperref","producer":"pdfTeX-1.40.21","creationdate":"2023-04-05T00:33:07+00:00","title":"","moddate":"2023-04-05T00:33:07+00:00","trapped":"/False","page_label":"10"}


created_at: 2026-06-08 08:18:55
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: 48a6f7d2-93fd-4415-9527-319d3b91c640
encoding: FLOAT32
metadata:
{"producer":"pdfTeX-1.40.21","creator":"LaTeX with hyperref","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","author":"","creationdate":"2023-04-05T00:33:07+00:00","chroma:document":"researchers increased from small to large, so we wanted to summarize existing\nresearch based on the size of the text word count. We divided the generated\ntext into three levels: phrases, sentences, and paragraphs.\nThe following article uses ChatGPT to generate phrases. Zhang et al. [22]\nproves that the semantic HAR model with semantic augmentation added dur-\ning training performs better in motion recognition than other models. Semantic","keywords":"","title":"","subject":"","trapped":"/False","page":9,"moddate":"2023-04-05T00:33:07+00:00","page_label":"10","total_pages":35}


created_at: 2026-06-08 08:18:55
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: 3d5deba4-ab8c-4739-972c-50293a03b768
encoding: FLOAT32
metadata:
{"subject":"","producer":"pdfTeX-1.40.21","trapped":"/False","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","page":9,"creator":"LaTeX with hyperref","chroma:document":"augmentation requires shared tokens, which is lacking in some datasets. There-\nfore, authors leverage ChatGPT for an automated label generation approach\nfor datasets originally without shared tokens. Fu et al. [23] describes a new\nworkﬂow for converting natural language commands into Bash commands. The\nauthor uses ChatGPT to generate a candidate list of Bash commands based\non user input, and then uses a combination of heuristic and machine learning","title":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","author":"","page_label":"10","moddate":"2023-04-05T00:33:07+00:00","creationdate":"2023-04-05T00:33:07+00:00","total_pages":35,"keywords":""}


created_at: 2026-06-08 08:18:55
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: 43a7bb49-af38-41b4-9612-f0e830745321
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{"producer":"pdfTeX-1.40.21","total_pages":35,"creator":"LaTeX with hyperref","keywords":"","moddate":"2023-04-05T00:33:07+00:00","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","page":9,"trapped":"/False","author":"","page_label":"10","chroma:document":"techniques to rank and select the most likely candidates. This workﬂow was\nevaluated on a real command dataset and achieved high accuracy compared\nto other state-of-the-art methods. Chen et al. [24] used the Bart model and\nChatGPT for the task of summarizing humorous titles and compared the perfor-\nmance of the two models. It was found that the Bart model performed better on\nlarge datasets, but ChatGPT was competitive with our best ﬁne-tuned model\nin a small range (48), albeit slightly weaker.","title":"","creationdate":"2023-04-05T00:33:07+00:00","subject":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2"}


created_at: 2026-06-08 08:18:55
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
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{"author":"","title":"","creationdate":"2023-04-05T00:33:07+00:00","page":9,"producer":"pdfTeX-1.40.21","moddate":"2023-04-05T00:33:07+00:00","subject":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","creator":"LaTeX with hyperref","page_label":"10","trapped":"/False","total_pages":35,"keywords":"","chroma:document":"in a small range (48), albeit slightly weaker.\nThe following article uses ChatGPT to generate sentences.Chen et al. [25]\nconstructed a dialogue dataset (HPD) with scenes, timelines, character at-\ntributes, and character relationships in order to use ChatGPT as a conver-\nsational agent to generate dialogue. However, ChatGPT’s performance on the\ntest set was poor, and there is room for improvement.In study [26], chatGPT\ndemonstrated its ability to simplify complex text by providing three ﬁctional","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2"}


created_at: 2026-06-08 08:18:55
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: e88a86a5-7880-401e-8abd-6eb25d4ed9e2
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created_at: 2026-06-08 08:18:55
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
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created_at: 2026-06-08 08:18:55
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: 4c4db1c2-0235-443c-9bc0-b788eb80af3b
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{"moddate":"2023-04-05T00:33:07+00:00","creator":"LaTeX with hyperref","page":10,"ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","page_label":"11","chroma:document":"with validation feedback to construct the model’s input. The eﬀectiveness of\nthe approach is veriﬁed using the QuixBugs dataset. The experiment shows\nthat ChatGPT ﬁne-tuned with reinforcement learning from human feedback\n(RLHF) outperforms Codex trained unsupervisedly in both repair datasets. In\nreference to study [28], ChatGPT was compared to three commercial transla-\ntion products: Google Translate2, DeepL Translate3, and Tencent TranSmart4.","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","keywords":"","total_pages":35,"trapped":"/False","subject":"","title":"","producer":"pdfTeX-1.40.21","creationdate":"2023-04-05T00:33:07+00:00","author":""}


created_at: 2026-06-08 08:18:55
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: 4cfffe55-7121-4e29-8b5d-e29eacd985d4
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{"creator":"LaTeX with hyperref","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","page_label":"11","chroma:document":"The evaluation was conducted on the Flores101 test set, using the WMT19\nbiomedical translation task to test translation robustness, with BLEU score as\nthe main metric. The study found that ChatGPT is competitive with commer-\ncial translation products on high-resource European languages but falls behind\non low-resource or distant languages. The authors explored an interesting strat-\negy called pivot prompts, which signiﬁcantly improved translation performance.","page":10,"creationdate":"2023-04-05T00:33:07+00:00","title":"","subject":"","moddate":"2023-04-05T00:33:07+00:00","producer":"pdfTeX-1.40.21","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","author":"","trapped":"/False","total_pages":35,"keywords":""}


created_at: 2026-06-08 08:18:55
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: 35a306e2-e0e4-4d73-a507-31061eaa70c7
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{"source":"./data/Summary of ChatGPTGPT-4 Research.pdf","page_label":"11","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","moddate":"2023-04-05T00:33:07+00:00","trapped":"/False","author":"","creator":"LaTeX with hyperref","subject":"","creationdate":"2023-04-05T00:33:07+00:00","total_pages":35,"title":"","chroma:document":"While ChatGPT did not perform as well as commercial systems on biomedical\nabstracts or Reddit comments, it may be a good speech translator. Prieto et\nal. [29] evaluated the use of ChatGPT in developing an automated construction\nschedule based on natural language prompts. The experiment required building\nnew partitions in an existing space and providing details on the rooms to be\npartitioned. The results showed that ChatGPT was able to generate a coher-","page":10,"producer":"pdfTeX-1.40.21","keywords":""}


created_at: 2026-06-08 08:18:55
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
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{"trapped":"/False","author":"","total_pages":35,"keywords":"","chroma:document":"ent schedule that followed a logical approach to meet the requirements of the\ngiven scope. However, there were still several major ﬂaws that would limit the\nuse of this tool in real-world projects.Michail et al. [30] proposed a method\nto improve the prediction accuracy of the HeFit ﬁne-tuned XLM T model on\ntweet intimacy by generating a dataset of tweets with intimacy rating tags using\nChatGPT. The speciﬁc operation is to input tweets with intimacy rating tags","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","title":"","moddate":"2023-04-05T00:33:07+00:00","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","page":10,"creator":"LaTeX with hyperref","page_label":"11","creationdate":"2023-04-05T00:33:07+00:00","subject":"","producer":"pdfTeX-1.40.21"}


created_at: 2026-06-08 08:18:55
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
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{"moddate":"2023-04-05T00:33:07+00:00","chroma:document":"into ChatGPT and then output similar tweets.\nThe following article uses ChatGPT to generate paragraphs. Wang et al.\n[31] compared the abstract summarization performance of ChatGPT and other\nmodels on various cross-lingual text datasets and found that ChatGPT may\nperform worse in metrics such as R 1, R 2, R L, and B S. Yang et al. [32]\nsummarized the performance of ChatGPT in question answering-based text\nsummarization and found that, compared to ﬁne-tuned models, ChatGPT’s","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","creator":"LaTeX with hyperref","page_label":"11","total_pages":35,"ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","title":"","trapped":"/False","creationdate":"2023-04-05T00:33:07+00:00","keywords":"","producer":"pdfTeX-1.40.21","author":"","subject":"","page":10}


created_at: 2026-06-08 08:18:55
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: f5d7ad6b-71e8-4d69-9c8c-d73e601cb35a
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{"author":"","creator":"LaTeX with hyperref","producer":"pdfTeX-1.40.21","trapped":"/False","page_label":"11","page":10,"chroma:document":"performance is slightly worse in all performance metrics. However, the article\nsuggests that if the dataset is golden annotation, ChatGPT’s performance may\nsurpass ﬁne-tuned models in these metrics. Belouadi et al. [33] compared the\nability of ByGPT5 and ChatGPT trained on a range of labeled and unlabeled\ndatasets of English and German poetry to generate constrained style poetry, and\nevaluated them using three metrics: Rhyme, ScoreAlliteration, and ScoreMe-","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","total_pages":35,"moddate":"2023-04-05T00:33:07+00:00","keywords":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","subject":"","title":"","creationdate":"2023-04-05T00:33:07+00:00"}


created_at: 2026-06-08 08:18:55
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: f7124c0a-d326-4ab3-a672-96e896e8714f
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{"moddate":"2023-04-05T00:33:07+00:00","author":"","trapped":"/False","keywords":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","total_pages":35,"creator":"LaTeX with hyperref","title":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","chroma:document":"ter Score. The conclusion is that ByGPT5 performs better than ChatGPT.\nBlanco-Gonzalez et al. [34] evaluated chatGPT’s ability to write commentary\narticles, and in fact, this article itself was written by chatGPT. The human au-\nthor rewrote the manuscript based on chatGPT’s draft. Experts found that it\ncan quickly generate and optimize text, as well as help users complete multiple\ntasks. However, in terms of generating new content, it is not ideal. Ultimately,","page":10,"creationdate":"2023-04-05T00:33:07+00:00","page_label":"11","subject":"","producer":"pdfTeX-1.40.21"}


created_at: 2026-06-08 08:18:55
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
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{"title":"","chroma:document":"it can be said that without strong human intervention, chatGPT is not a use-\nful tool for writing reliable scientiﬁc texts. It lacks the knowledge and expertise\nrequired to accurately and fully convey complex scientiﬁc concepts and informa-\n11","creationdate":"2023-04-05T00:33:07+00:00","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","creator":"LaTeX with hyperref","page_label":"11","subject":"","trapped":"/False","author":"","producer":"pdfTeX-1.40.21","keywords":"","page":10,"moddate":"2023-04-05T00:33:07+00:00","total_pages":35}


