The DOI matches its Crossref record; 201 of the 207 references it cites were found in a registry or an index, and 6 are not checked yet; none of the 117 sources its claims cite is checked for support yet.
Decoding ChatGPT: A taxonomy of existing research, current challenges, and possible future directions
Open accessAbstract
Chat Generative Pre-trained Transformer (ChatGPT) has gained significant interest and attention since its launch in November 2022. It has shown impressive performance in various domains, including passing exams and creative writing. However, challenges and concerns related to biases and trust persist. In this work, we present a comprehensive review of over 100 Scopus-indexed publications on ChatGPT, aiming to provide a taxonomy of ChatGPT research and explore its applications. We critically analyze the existing literature, identifying common approaches employed in the studies. Additionally, we investigate diverse application areas where ChatGPT has found utility, such as healthcare, marketing and financial services, software engineering, academic and scientific writing, research and education, environmental science, and natural language processing. Through examining these applications, we gain valuable insights into the potential of ChatGPT in addressing real-world challenges. We also discuss crucial issues related to ChatGPT, including biases and trustworthiness, emphasizing the need for further research and development in these areas. Furthermore, we identify potential future directions for ChatGPT research, proposing solutions to current challenges and speculating on expected advancements. By fully leveraging the capabilities of ChatGPT, we can unlock its potential across various domains, leading to advancements in conversational AI and transformative impacts in society.
Checked 2026-10-06 · 6 references not checked yet
Overview
AI snapshot
Written from the paper’s full text.
TL;DR
This review synthesizes 109 Scopus-indexed ChatGPT publications up to March 25, 2023, proposing a taxonomy across healthcare, marketing/finance, software engineering, academic/scientific writing, research/education, environmental science, and NLP. It identifies intrinsic and usage-related challenges—hallucination, bias, misinformation, copyright, over-reliance—and future directions in conversational ability, personalization, multimodal design, and trustworthiness.
Key findings
- Of 109 included articles from 349 authors across 53 nations, medicine was the largest subject area at 23%, followed by social sciences at 20% and computer science at 11%; the United States contributed 33 publications, the United Kingdom 10, and Australia and China 9 each.
- The corpus comprised 68 evaluations, 39 prediction articles, and 10 reviews; most evaluations focused on scientific writing (30 publications), followed by education and research ability.
- Applications span healthcare, marketing/financial services, software engineering, academic/scientific writing, research/education, environmental science, and NLP; for example, ChatGPT’s bug-fixing was comparable to CoCoNut and Codex and superior to standard approaches, and in one abstract-generation experiment 68% of generated abstracts were correctly identified while 14% of real abstracts were mistakenly identified as generated.
- Challenges are categorized as intrinsic (hallucination, biased content, not real-time, misinformation, inexplicability) and usage-related (ethical issues, copyright violation, over-reliance); future directions include improving conversational capabilities, personalization, multimodal design, and trustworthiness.
Methods
- Systematic literature review following Kitchenham (2004), searching Scopus with inclusion criteria: English, peer-reviewed, keyword “chatgpt” or “chat-gpt” in abstract/title, discusses ChatGPT, and published by March 25, 2023; exclusions included short papers, duplicates, and earlier versions with errata.
- A total of 109 articles were included and analyzed by subject area, country/collaboration, publication trends, taxonomy, applications, challenges, and future directions.
Limitations
- The paper states ChatGPT’s own limitations—intrinsic issues such as hallucination, biased content, not being real-time, misinformation, and inexplicability, plus usage-related issues such as ethical concerns, copyright violation, and over-reliance—and notes that early ChatGPT research still faces limitations.
- The paper does not explicitly label limitations of the review itself; evident from the methodology, the search is limited to Scopus, English, peer-reviewed publications up to March 25, 2023, excluding short papers, duplicates, and errata, making coverage database-specific and time-bound.
Model-written summary. Check it against the paper before relying on it.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
86 Not checked yet · 27 Own result · 27 No basis given · 12 Own interpretation · 2 Own method
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Notably, the field of medicine had the highest representation, accounting for 23% of the total publications.”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Rests on an assumption: The Scopus database and the search queries used capture the relevant literature on ChatGPT.
- Rests on an assumption: The 109 articles included from Scopus are representative of the broader literature on ChatGPT.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This was followed by social sciences (20%) and computer science (11%) (see Figure 1).”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Rests on an assumption: The Scopus database and the search queries used capture the relevant literature on ChatGPT.
- Rests on an assumption: The 109 articles included from Scopus are representative of the broader literature on ChatGPT.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“It was followed by the United Kingdom with 10 publications, and Australia and China with 9 publications each.”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Rests on an assumption: The Scopus database and the search queries used capture the relevant literature on ChatGPT.
- Rests on an assumption: The 109 articles included from Scopus are representative of the broader literature on ChatGPT.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This was followed by social sciences (20%) and computer science (11%) (see Figure 1).”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Rests on an assumption: The Scopus database and the search queries used capture the relevant literature on ChatGPT.
- Rests on an assumption: The 109 articles included from Scopus are representative of the broader literature on ChatGPT.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The United States ranked first as the country of origin for published articles, with a total of 33 publications in the corpus.”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Rests on an assumption: The Scopus database and the search queries used capture the relevant literature on ChatGPT.
- Rests on an assumption: The 109 articles included from Scopus are representative of the broader literature on ChatGPT.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“After applying the specified criteria, a total of 109 articles were included in the analysis.”
- Also stated in: § IV. RECENT TREND OF PUBLICATIONS RELATED TO CHATGPT · p. 4 · plain
“A thorough literature review was conducted, and 109 articles were found after searching the Scopus database for relevant articles on ChatGPT.”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Rests on an assumption: The Scopus database and the search queries used capture the relevant literature on ChatGPT.
- Rests on an assumption: The 109 articles included from Scopus are representative of the broader literature on ChatGPT.
- Also stated in: § IV. RECENT TREND OF PUBLICATIONS RELATED TO CHATGPT · p. 4 · plain
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Making predictions using ChatGPT for different fields is the subject of the second-largest group of articles (39) and the fewest reviews (10 publications).”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Rests on an assumption: The Scopus database and the search queries used capture the relevant literature on ChatGPT.
- Rests on an assumption: The 109 articles included from Scopus are representative of the broader literature on ChatGPT.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Upon reviewing the entire body of literature related to ChatGPT, a prevalent pattern has emerged in the structure of most articles, as shown in Figure 3. Typically, authors select a topic of interest, ask ChatGPT questions, and then provide interpretations based on its responses.”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Rests on an assumption: The Scopus database and the search queries used capture the relevant literature on ChatGPT.
- Rests on an assumption: The 109 articles included from Scopus are representative of the broader literature on ChatGPT.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“It is noted that the majority of authors have evaluated ChatGPT for its capacity for scientific writing (30 publications), whether in terms of producing data Maddigan & Susnjak (2023), writing a scientific article D'Amico et al. (2023), Anderson et al. (2023), Farhat et al. (2023), or fetching references for particular subjects Gravel et al. (2023), Alkaissi & McFarlane (2023), Farhat et al. (2023”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Rests on an assumption: The Scopus database and the search queries used capture the relevant literature on ChatGPT.
- Rests on an assumption: The 109 articles included from Scopus are representative of the broader literature on ChatGPT.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Following the classification of the retrieved articles using either their abstracts or full texts, it was discovered that there were mainly three categories of articles published up until March 25th, 2023: 1) evaluations of ChatGPT, 2) predictions made using ChatGPT, and 3) reviews on ChatGPT.”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Rests on an assumption: The Scopus database and the search queries used capture the relevant literature on ChatGPT.
- Rests on an assumption: The 109 articles included from Scopus are representative of the broader literature on ChatGPT.
- Probably not a substantial claim. 80
- It may state two separate claims as one. 39
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Multidisciplinary subjects and health professionals made up 8% and 7% of the total corpus, respectively.”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Rests on an assumption: The Scopus database and the search queries used capture the relevant literature on ChatGPT.
- Rests on an assumption: The 109 articles included from Scopus are representative of the broader literature on ChatGPT.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“These articles involved contributions from 349 authors representing 53 different nations, indicating a wide range of international participation in the literature on ChatGPT.”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Rests on an assumption: The Scopus database and the search queries used capture the relevant literature on ChatGPT.
- Rests on an assumption: The 109 articles included from Scopus are representative of the broader literature on ChatGPT.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Multidisciplinary subjects and health professionals made up 8% and 7% of the total corpus, respectively.”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Rests on an assumption: The Scopus database and the search queries used capture the relevant literature on ChatGPT.
- Rests on an assumption: The 109 articles included from Scopus are representative of the broader literature on ChatGPT.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“It was followed by the United Kingdom with 10 publications, and Australia and China with 9 publications each.”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Rests on an assumption: The Scopus database and the search queries used capture the relevant literature on ChatGPT.
- Rests on an assumption: The 109 articles included from Scopus are representative of the broader literature on ChatGPT.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“As a result, many authors have expressed concerns regarding the ethical and scientific implications of ChatGPT's features and their potential negative effects on scientific academia.”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
- Rests on an assumption: The Scopus database and the search queries used capture the relevant literature on ChatGPT.
- Rests on an assumption: The 109 articles included from Scopus are representative of the broader literature on ChatGPT.
None of the sources cited for this claim have been checked yet.
In the paper
“Some have also asked questions based on their extensive understanding of various fields, including parasitology Huh (2023 b ), ˇ Slapeta (2023), clinical diagnosis Hirosawa et al. (2023), environmental sciences Rillig et al. (2023), public health Biswas (2023 e ), etc.”
What the sources say
- Not checked yetcites Huh (2023 b )
- Not checked yetcites ˇ Slapeta (2023)
- Not checked yetcites Hirosawa et al. (2023)
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Rests on an assumption: The Scopus database and the search queries used capture the relevant literature on ChatGPT.
- Rests on an assumption: The 109 articles included from Scopus are representative of the broader literature on ChatGPT.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Meanwhile, several articles have explored how ChatGPT could impact learners and educators, as well as the ethical considerations that must be taken into account when using it.”
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
- Rests on an assumption: The Scopus database and the search queries used capture the relevant literature on ChatGPT.
- Rests on an assumption: The 109 articles included from Scopus are representative of the broader literature on ChatGPT.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Iraqi Journal for Computer Science and Mathematics has also published 2 documents as well.”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Rests on an assumption: The Scopus database and the search queries used capture the relevant literature on ChatGPT.
- Rests on an assumption: The 109 articles included from Scopus are representative of the broader literature on ChatGPT.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Furthermore, it has been noted that the majority of publications center around either ChatGPT's feature assessment as a scientific writing assistant or predictions about the future of the education system.”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Rests on an assumption: The Scopus database and the search queries used capture the relevant literature on ChatGPT.
- Rests on an assumption: The 109 articles included from Scopus are representative of the broader literature on ChatGPT.
None of the sources cited for this claim have been checked yet.
In the paper
“The different assessments included those for bias Wang (2023 b ), Wang, Miao, Li, Wang & Lin (2023), for study Cooper (2023), for mental health Prada et al. (2023), for public health Jungwirth & Haluza (2023 a ); Biswas (2023 e ), etc.”
What the sources say
- Not checked yetcites Wang (2023 b )
- Not checked yetcites Cooper (2023) source
- Not checked yetcites Prada et al. (2023)
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
- Rests on an assumption: The Scopus database and the search queries used capture the relevant literature on ChatGPT.
- Rests on an assumption: The 109 articles included from Scopus are representative of the broader literature on ChatGPT.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Clinicians and medical professionals probed ChatGPT with hypothetical or real-world patient symptoms to determine whether or not it would aid in the diagnosis or treating patients.”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Rests on an assumption: The Scopus database and the search queries used capture the relevant literature on ChatGPT.
- Rests on an assumption: The 109 articles included from Scopus are representative of the broader literature on ChatGPT.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Next, Accountability in Research has published 4 articles.”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Rests on an assumption: The Scopus database and the search queries used capture the relevant literature on ChatGPT.
- Rests on an assumption: The 109 articles included from Scopus are representative of the broader literature on ChatGPT.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Switzerland ranked second in terms of collaboration, with 20 collaborative countries, followed by Australia with 19 collaborations, and the United Kingdom with 18 collaborations (see Figure 2).”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Rests on an assumption: The Scopus database and the search queries used capture the relevant literature on ChatGPT.
- Rests on an assumption: The 109 articles included from Scopus are representative of the broader literature on ChatGPT.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In terms of collaborations, the United States had the most extensive network, collaborating with 24 different countries, accounting for over 18% of the total corpus.”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Rests on an assumption: The Scopus database and the search queries used capture the relevant literature on ChatGPT.
- Rests on an assumption: The 109 articles included from Scopus are representative of the broader literature on ChatGPT.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“These interpretations can be broadly categorized into two types: those that predict the future of the topic in the context of ChatGPT and those that assess ChatGPT's features and their potential impacts.”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Rests on an assumption: The Scopus database and the search queries used capture the relevant literature on ChatGPT.
- Rests on an assumption: The 109 articles included from Scopus are representative of the broader literature on ChatGPT.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“It was followed by the United Kingdom with 10 publications, and Australia and China with 9 publications each.”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Rests on an assumption: The Scopus database and the search queries used capture the relevant literature on ChatGPT.
- Rests on an assumption: The 109 articles included from Scopus are representative of the broader literature on ChatGPT.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Ethical considerations are discussed in the majority of the papers either in detail or briefly.”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
- Rests on an assumption: The Scopus database and the search queries used capture the relevant literature on ChatGPT.
- Rests on an assumption: The 109 articles included from Scopus are representative of the broader literature on ChatGPT.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Despite its potential, the early ChatGPT researches still face some limitations.”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Additionally, we have identified and discussed ethical concerns.”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“We have identified certain issues that may need to be addressed, which are classified as intrinsic and usage-centric.”
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Subsequent versions, such as GPT-2, GPT3, and the latest release, GPT-3.5, have experienced notable advancements by significantly augmenting the number of parameters. This augmentation has facilitated the generation of responses that are even more accurate and human-like.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
In the paper
“ChatGPT utilizes a transformer architecture consisting of encoder-decoder layers that collaborate to process and generate natural language text Chen, Yu, Yang, Guo, Bian & Wu (2023).”
What the sources say
- Not checked yetcites Chen, Yu, Yang, Guo, Bian & Wu (2023)
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“The transformer architecture of ChatGPT consists of two main components: the encoder and the decoder Budzianowski & Vulic (2019).”
What the sources say
- Not checked yetcites Budzianowski & Vulic (2019)
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“ChatGPT is turning out to be a Code Assistant and Debugger. It helps software developers by providing code suggestions, debugging assistance, and answering programming-related questions Aljanabi, Ghazi, Ali, Abed et al. (2023).”
What the sources say
- Not checked yetcites Aljanabi, Ghazi, Ali, Abed et al. (2023)
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“The initial iteration of ChatGPT referred to as GPT-1, was equipped with 117 million parameters and underwent training on a substantial corpus of text data Ernst & Bavota (2022).”
What the sources say
- Not checked yetcites Ernst & Bavota (2022) source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“To continually improve the reliability and accuracy of the model, ChatGPT incorporates reinforcement learning from human feedback (RLHF), allowing it to learn and understand human preferences through extended dialogues Christiano et al. (2017), Stiennon et al. (2020).”
What the sources say
- Not checked yetcites Christiano et al. (2017)
- Not checked yetcites Stiennon et al. (2020)
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“ChatGPT has the ability to generate new responses or utilize pre-existing ones Salvagno et al. (2023).”
What the sources say
- Not checked yetcites Salvagno et al. (2023)
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“Researchers have been exploring and highlighting the potential harms associated with ChatGPT, including the propagation of stereotypes, biased responses, and dissemination of misleading information Liang et al. (2021), Nadeem et al. (2020).”
What the sources say
- Not checked yetcites Liang et al. (2021)
- Not checked yetcites Nadeem et al. (2020)
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“In marketing, ChatGPT works as a conversational chatbot to handle customer inquiries, provide product recommendations, and assist with order tracking.”
What the sources say
- Not checked yetcites Cascella et al. (2023 a )
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“An essential feature of the transformer architecture is the attention mechanism, which allows the model to selectively focus on different parts of the input text while generating the output Jain et al. (n.d.).”
What the sources say
- Not checked yetcites Jain et al. (n.d.)
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“Moreover, there are concerns that ChatGPT-generated content may infringe upon copyright and intellectual property rights Deng & Lin (2022).”
What the sources say
- Not checked yetcites Deng & Lin (2022)
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
None of the sources cited for this claim have been checked yet.
In the paper
“Following its launch, ChatGPT quickly gained a lot of popularity in a variety of disciplines Cox (2023), Dwivedi et al. (2023), Tlili et al. (2023 a ), including academia Chen (2023 b ) and science Morreel et al. (2023).”
What the sources say
- Not checked yetcites Cox (2023)
- Not checked yetcites Dwivedi et al. (2023) source
- Not checked yetcites Tlili et al. (2023 a )
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Rests on an assumption: The rapid dissemination of ChatGPT research resulted in a diverse research landscape due to its wide publicity and acceptance at different levels in daily life.
In the paper
“although it occasionally noticed and corrected them.”
What the sources say
- Not checked yetcites Howard et al. (2023)
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“ChatGPT also had deficits in situational awareness, inference, and consistency”
What the sources say
- Not checked yetcites Howard et al. (2023)
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“and it did not repeat the same advice in a single response.”
What the sources say
- Not checked yetcites Howard et al. (2023)
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“ChatGPT's responses were coherent”
What the sources say
- Not checked yetcites Howard et al. (2023)
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“The model's answers included a summary of its understanding of the scenario and question, management options, and disclaimers that reflected its information sources, which were similar to the format of patient information websites.”
What the sources say
- Not checked yetcites Howard et al. (2023)
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“ChatGPT also had deficits in situational awareness, inference, and consistency”
What the sources say
- Not checked yetcites Howard et al. (2023)
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“This feature can enhance the coding experience for less-skilled programmers, making it more user-friendly and intuitive Aljanabi, Ghazi, Ali, Abed et al. (2023).”
What the sources say
- Not checked yetcites Aljanabi, Ghazi, Ali, Abed et al. (2023)
plainconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“source control was often disregarded.”
What the sources say
- Not checked yetcites Howard et al. (2023)
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“To conduct our literature review, we adopted the methodology proposed in Kitchenham (2004).”
What the sources say
- Not checked yetcites Kitchenham (2004)
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Rests on an assumption: The Scopus database and the search queries used capture the relevant literature on ChatGPT.
In the paper
“while its antimicrobial spectra and regimens were appropriate”
What the sources say
- Not checked yetcites Howard et al. (2023)
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“ChatGPT also had deficits in situational awareness, inference, and consistency”
What the sources say
- Not checked yetcites Howard et al. (2023)
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“We have also performed a screening of the articles by going through the abstract and assessing its relevance to the title under consideration.”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Rests on an assumption: The Scopus database and the search queries used capture the relevant literature on ChatGPT.
None of the sources cited for this claim have been checked yet.
In the paper
“Research scholars have been using ChatGPT in a variety of ways, including writing scientific literature, gathering data, drafting abstracts for research papers Else (2023), and looking for medical diagnostic advice Hirosawa et al. (2023).”
What the sources say
- Not checked yetcites Else (2023) source
- Not checked yetcites Hirosawa et al. (2023)
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“their spelling and grammar were appropriate”
What the sources say
- Not checked yetcites Howard et al. (2023)
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“Clinicians often rely on complex information to make decisions, but in telemedicine, the available information is typically limited to language only, making it a potential candidate for interventions using LLM Roosan et al. (2016).”
What the sources say
- Not checked yetcites Roosan et al. (2016)
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“The information provided by ChatGPT was consistent”
What the sources say
- Not checked yetcites Howard et al. (2023)
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“ChatGPT has a significant benefit in coding, as it can process human inputs, allowing software developers to supply code snippets or commands in a conversational way rather than providing specific keywords or phrases.”
What the sources say
- Not checked yetcites Aljanabi, Ghazi, Ali, Abed et al. (2023)
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“ChatGPT often repeated questions verbatim, including any errors”
What the sources say
- Not checked yetcites Howard et al. (2023)
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“After running search queries, inclusion (IC) and exclusion (EC) criteria were created to filter out the retrieved articles from the Scopus database.”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
- Rests on an assumption: The Scopus database and the search queries used capture the relevant literature on ChatGPT.
None of the sources cited for this claim have been checked yet.
In the paper
“There are existing review works on Large Language Models (LLM) Melis et al. (2017), Chen et al. (2021) and AI Generated Content (AIGC) Cao et al. (2023) but they are very broad and do not take into consideration the specificities related to ChatGPT.”
What the sources say
- Not checked yetcites Melis et al. (2017)
- Not checked yetcites Chen et al. (2021)
- Not checked yetcites Cao et al. (2023)
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“to contribute to the advance- ment of gastroenterology by generating high-quality re- search questions”
What the sources say
- Not checked yetcites Lahat et al. (2023)
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“NLP technologies are being used to create conversational chatbots to mimic the con- versational proficiency of hu- mans.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
In the paper
“The findings revealed that task-specific models like RoBERTa outperformed ChatGPT, particularly in specialized downstream tasks. However, ChatGPT still demonstrated satisfactory performance compared to baseline models in a variety of tasks.”
What the sources say
- Not checked yetcites Amin et al. (2023)
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Use ChatGPT's information can be updated and cor- rected, but it may not always reflect the latest knowledge.”
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
In the paper
“In fact, few publishers have recognized the use of ChatGPT for academic writing to promote its ethical usage practices Editorialge (Jan, 2023).”
What the sources say
- Not checked yetcites Editorialge (Jan, 2023)
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“ChatGPT potential to support decision-making in Japanese healthcare settings, but cau- tion is needed due to per- formance improvements re- quired.”
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“Unethical use also includes the deliberate generation of manipulated content that can promote misinformation and provoke violence, creating damage at an individual or organizational level Elali & Rachid (2023), Cao et al. (2023).”
What the sources say
- Not checked yetcites Elali & Rachid (2023)
- Not checked yetcites Cao et al. (2023)
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Researchers have identified several issues regarding ChatGPT, which can be broadly categorized into two groups: intrinsic limitations and usage-related concerns.”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
In the paper
“Qin et al. (2023) argued that larger models like ChatGPT can perform NLP tasks without the need for specific data adaptation.”
What the sources say
- Not checked yetcites Qin et al. (2023)
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“Prolonged questioning could be beneficial in enhanc- ing and improving both the kinds of human reasoning and argumentation imitated by current LLMs.”
What the sources say
- Not checked yetcites Cox & Tzoc (2023)
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“ChatGPT can act as a user assistance tool, provid- ing guidance, explanations, and educational resources to users interacting with robots.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
None of the sources cited for this claim have been checked yet.
In the paper
“The knowledge and ability to analyze results of para- sitology test by ChatGPT were not yet on par with those of Korean medical stu- dents”
What the sources say
- Not checked yetcites Huh (2023 c , a )
- Not checked yetcites ˇ Slapeta (2023)
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“There are also concerns that ChatGPT-generated content may lead to violations of copyright and intellectual property rights Sallam (2023).”
What the sources say
- Not checked yetcites Sallam (2023) source
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“ChatGPT's knowl- edge and interpretation in the Chinese Chinese Na- tional Medical Licensing Ex- amination (NMLE) are be- low medical students' level”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
In the paper
“The correct diagnoses within ten differential-diagnosis lists generated by ChatGPT was more than 90%.”
What the sources say
- Not checked yetcites Hirosawa et al. (2023)
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“These limitations make the usage and deployment of ChatGPT difficult in real-world scenarios.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“It can recognize when a word, phrase, or sentence has mul- tiple possible interpretations, leading to potential confu- sion or miscommunication.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“Rahman & Watanobe (2023) has experimentally shown that ChatGPT can be used to solve both technical problems, such as engineering and computer programming, and non-technical problems, such as language and literature.”
What the sources say
- Not checked yetcites Rahman & Watanobe (2023)
boosteddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“ChatGPT can aid in diag- nosing issues and providing troubleshooting guidance for robotic systems.”
plainconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“It includes five major limitations, namely, hallucination, biased content, not real-time, misinformation and inexplicability.”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“It is worth noting that the detection of artificially generated fake text and meaningless information is not a new challenge Amancio (2015), Jawahar et al. (2020).”
What the sources say
- Not checked yetcites Amancio (2015)
- Not checked yetcites Jawahar et al. (2020)
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“ChatGPT can help establish shared un- derstandings, allocate tasks, and assist in real-time com- munication, thereby improv- ing the overall efficiency and performance of robotic teams.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“Additionally, the application of ChatGPT is rapidly expanding across various domains Mijwil et al. (2023), Aljanabi, Ghazi, Ali, Abed et al. (2023), indicating a growing trend that is expected to continue in the foreseeable future.”
What the sources say
- Not checked yetcites Mijwil et al. (2023)
- Not checked yetcites Aljanabi, Ghazi, Ali, Abed et al. (2023)
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Both issues can lead to the creation of counterfactual or meaningless responses, which can seriously threaten the reliability of the generated content.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“68% of the generated abstracts were correctly identified as such, and 14% of the real abstracts were mistakenly identified as generated by ChatGPT.”
What the sources say
- Not checked yetcites Gao, Tong, Wu, Chen, Zhu & Wang (2023)
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The category of usage-related issues includes unethical usage of the tool, copyright-infringed content, and overreliance on ChatGPT.”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“The latter process, which is fake text identification, falls within a well-established domain, with many state-of-the-art algorithms and techniques available to tag fake text with significant accuracy Zhou & Zafarani (2020).”
What the sources say
- Not checked yetcites Zhou & Zafarani (2020) source
boosted- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“The evaluation results indicated that ChatGPT excelled in tasks that required reasoning skills, including arithmetic reasoning while facing challenges in tasks such as sequence tagging.”
What the sources say
- Not checked yetcites Qin et al. (2023)
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“ChatGPT helps identify in- stances of linguistic ambi- guity in text or speech.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“ChatGPT can help assess the risk level of individuals experiencing mental health crises by analyzing conversations.”
plainconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
In the paper
“ChatGPT performs well in structured tasks such as translating code and explaining wellknown concepts but struggles with nuanced tasks such as explaining less familiar terms and creating code from scratch.”
What the sources say
- Not checked yetcites Megahed et al. (2023)
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Given the similarities among various NLP tasks, the findings from the aforementioned studies can be extrapolated to other related areas, including the assessment of news article accuracy, especially in the detection of fake news.”
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“The results demonstrated that GPT models could generate translation outputs that were highly competitive for languages with abundant resources. However, for low-resource languages, the current state of GPT models exhibited limitations, indicating the need for further improvements.”
What the sources say
- Not checked yetcites Hendy et al. (2023)
boostedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“Lastly, there is a fear of overreliance on the tool, which may make humans lazy and apathetic and make them always rely on the generated information Sallam et al. (2023).”
What the sources say
- Not checked yetcites Sallam et al. (2023) source
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“Major publishers like Elsevier, who publish the Lancet family of journals, have already stated that AI cannot be listed as an author and that its use must be properly acknowledged Sallam et al. (2023).”
What the sources say
- Not checked yetcites Sallam et al. (2023) source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Our view is that ChatGPT's availability, ease of use, and multi-language capabilities could significantly boost scholarly output, thereby democratizing knowledge dissemination.”
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The quality of the data is also crucial, and it is important to ensure that the data used for training is accurate, relevant, and diverse enough to represent the full range of language and speech patterns.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. It may state two separate claims as one.
In the paper
“By comparing their model with an approach based on perplexity scores, they found that differentiating between human-generated and ChatGPT-generated reviews is more challenging for the machine-learning model when using rephrased text.”
What the sources say
- Not checked yetcites Mitrovic et al. (2023)
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“As a result, adding feelings to AI language models runs the risk of unintended consequences and risks like bias and discrimination, manipulation, privacy evasion, and inappropriate or offensive responses.”
plainconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Additionally, other factors such as the model architecture, training methods, and hyperparameters can also impact the model's performance.”
plainconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
None of the sources cited for this claim have been checked yet.
In the paper
“However, the former step, which focuses on specifically identifying texts generated by ChatGPT, is relatively new, and researchers are actively working to address this issue Dugan et al. (2022), Mitchell et al. (2023), Curtis (2023), Mitrovic et al. (2023).”
What the sources say
- Not checked yetcites Dugan et al. (2022)
- Not checked yetcites Mitchell et al. (2023)
- Not checked yetcites Curtis (2023)
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Incorporating human feedback is an effective way to improve the performance of ChatGPT.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Arguably, effective conversational AI systems like ChatGPT require a combination of various techniques and considerations to deliver a satisfying and human-like conversational experience, for example, considering factors such as context understanding, coherence, and response relevance.”
hedgeddirect- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
None of the sources cited for this claim have been checked yet.
In the paper
“In the case of ChatGPT, fine-tuning allows the model to learn and understand the nuances of natural language conversations, enabling it to generate more human-like responses.”
What the sources say
- Not checked yetcites Bakker et al. (2022)
- Not checked yetcites Himeur et al. (2022 b )
- Not checked yetcites Dwivedi et al. (2023) source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“However, it is also important to note that simply increasing the volume and variety of data alone may not always result in improved performance.”
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Improving Conversational Capabilities, Personalization, Multimodal design, and Trustworthiness are some of the key areas that hold significant promise for the future of ChatGPT.”
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. It may state two separate claims as one.
The paper relies on this without support.
The Scopus database and the search queries used capture the relevant literature on ChatGPT.
“After running search queries, inclusion (IC) and exclusion (EC) criteria were created to filter out the retrieved articles from the Scopus database.”
The paper relies on this without support.
The rapid dissemination of ChatGPT research resulted in a diverse research landscape due to its wide publicity and acceptance at different levels in daily life.
“The rapid dissemination of ChatGPT research resulted in a diverse research landscape due to its wide publicity and acceptance at different levels in our daily lives.”
The paper relies on this without support.
The 109 articles included from Scopus are representative of the broader literature on ChatGPT.
“After applying the specified criteria, a total of 109 articles were included in the analysis.”
The paper relies on this without support.
ChatGPT has the capability to automatically generate responses by drawing information from numerous internet sources, often without requiring further input from the user.
“ChatGPT has the capability to automatically generate responses by drawing information from numerous internet sources, often without requiring further input from the user.”
The paper relies on this without support.
ChatGPT is unaware of copyright materials.
“Since ChatGPT is unaware of copyright materials, verifying the generated content before using/publishing and tagging by humans in case of copyright violation might help to resolve this issue.”
The paper relies on this without support.
Continuous training and updating of ChatGPT requires ongoing resources and infrastructure.
“Additionally, it is important to note that continuous training and updating of ChatGPT requires ongoing resources and infrastructure to support it.”
The paper relies on this without support.
Speech recognition technology enables ChatGPT to comprehend and recognize spoken words.
“By processing aural input and translating it into text that software or other devices may use, speech recognition technology enables ChatGPT to comprehend and recognize spoken words.”
The paper relies on this without support.
Video content analysis allows a multimodal ChatGPT to analyze and process video footage, detect objects, and track motions.
“By incorporating video content analysis, a multimodal ChatGPT can analyze and process video footage, detect objects, and track motions within the video.”
The paper relies on this without support.
Image captioning can be used to add descriptive or explanatory text to images.
“Similarly, image captioning can be used to add descriptive or explanatory text to images, which can help to make them more accessible and informative for viewers.”
The paper relies on this without support.
Audio captioning can help make audio content more accessible.
“It would be beneficial for persons who are deaf or hard of hearing, as well as nonnative speakers, to add text subtitles to audio information or audio captioning because it can help make audio content more accessible.”
The paper relies on this without support.
Video captioning can provide closed captions or subtitles during conversations.
“Video captioning can be added to ChatGPT to provide closed captions or subtitles during conversations, making the video content more accessible.”
The paper relies on this without support.
Video indexing allows users to search for specific content within a video using keywords or timestamps.
“Additionally, video indexing allows users to search for specific content within a video using keywords or timestamps, making it easier to find relevant information quickly.”
The paper relies on this without support.
ChatGPT is designed to generate responses based on the input it receives.
“As an AI language model, ChatGPT is designed to generate responses based on the input it receives.”
The paper relies on this without support.
ChatGPT does not have the ability to actively disclose information about itself or the data it uses to generate responses.
“While it can provide information and insights, it does not have the ability to actively disclose information about itself or the data it uses to generate responses.”
The paper relies on this without support.
ChatGPT may not always provide the most nuanced or complete answer to a question, and it may occasionally make mistakes or provide incorrect information.
“As such, it may not always provide the most nuanced or complete answer to a question, and it may occasionally make mistakes or provide incorrect information.”
The paper relies on this without support.
Transparent AI systems are simpler to comprehend and analyze, they encourage trust and make it more likely that the system will make ethical and legal choices.
“Because transparent AI systems are simpler to comprehend and analyze, they encourage trust and make it more likely that the system will make ethical and legal choices.”
The paper relies on this without support.
Humans can comprehend and validate an explainable AI's decision-making process, which is crucial for building confidence in AI systems.
“As a result, humans can comprehend and validate the system's decision-making process, which is crucial for building confidence in AI systems.”
The paper relies on this without support.
Human-Centered Design principles are usually applied in the research and development process of AI language models.
“In the case of developing AI language models, including ChatGPT, Human-Centered Design principles are usually applied in the research and development process, such as conducting user research to understand the needs and behaviors of people who use language models, testing different design prototypes, and iterating on the design based on user feedback.”
The paper relies on this without support.
ChatGPT can provide information and insights.
“While it can provide information and insights, it does not have the ability to actively disclose information about itself or the data it uses to generate responses.”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“The majority of existing work consists of conceptual and opinion-based discussions (e.g., Farrelly & Baker, 2023;Hsu & Ching., 2023;Sohail et al., 2023;Yu, 2023), along with quantitative surveys aimed at specific groups (e.g., Hammour et al., 2023;Tangadulrat et al., 2023).”
From Widen the debate: What is the academic community’s perception on ChatGPT? (2024) · cites this paper as Sohail et al., 2023
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ C. Research Questions and Prime Contributions · p. 3
“Section 4 discusses the applications of ChatGPT. Limitations of the tool and future enhancements are outlined in Section 5 and Section 6, respectively. Finally, concluding remarks are presented in Section 7.”
§ V. APPLICATIONS OF CHATGPT · p. 6
“## V. APPLICATIONS OF CHATGPT”
Citations
Cites
3 retracted
Retracted: Chat With ChatGPT on Intelligent Vehicles: An IEEE TIV Perspective
Du, H., Teng, S., Chen, H., Ma, J., Wang, X., Gou, C., Li, B., Ma, S., Miao, Q., Na, X., et al. (2023) · IEEE Transactions on Intelligent Vehicles · doi:10.1109/tiv.2023.3253281 · Cited by 164
RetractedRetracted: Chat With ChatGPT on Interactive Engines for Intelligent Driving
Gao, Y., Tong, W., Wu, E. Q., Chen, W., Zhu, G., & Wang, F. (2023) · IEEE Transactions on Intelligent Vehicles · doi:10.1109/tiv.2023.3252571 · Cited by 84 · cited 2×
“The authors in Gao et al. (2023) collected a total of 50 research abstracts by selecting ten abstracts from five prestigious medical journals with high impact factors.”
RetractedZhang, J., Pu, J., Xue, J., Yang, M. Z., Xu, X., Wang, X., & Wang, F. (2023) · IEEE Transactions on Intelligent Vehicles · doi:10.1109/tiv.2023.3256982 · Cited by 71
RetractedChatGPT for Good? On Opportunities and Challenges of Large Language Models for Education
Kasneci, E., Seßler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., Gasser, U., Groh, G., Günnemann, S., Hüllermeier, E., et al. (2023) · doi:10.35542/osf.io/5er8f · Cited by 7,552
ChatGPT for good? On opportunities and challenges of large language models for education
Kasneci, E., Sessler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., Gasser, U., Groh, G., Günnemann, S., Hüllermeier, E., et al. (2023) · Learning and Individual Differences · doi:10.1016/j.lindif.2023.102274 · Cited by 7,326 · cited 1×
“(Perkins, 2023; Wang et al., 2023; Kasai et al., 2023; Gilson et al., 2022; Kaneda et al., 2023; Wu et al., 2023; Bhayana et al., (Lund et al., 2023a) Learning Foreign Languages Explore the development of chatbot systems and the principal approaches and data sets employed in their creation.”
(2004) · Cited by 5,198
Dwivedi, Y. K., Ismagilova, E., Hughes, D. L., Carlson, J., Filieri, R., Jacobson, J., Jain, V., Karjaluoto, H., Kefi, H., Krishen, A. S., et al. (2021) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2020.102168 · Cited by 4,608 · cited 1×
“More diverse data and a deeper comprehension of linguistic nuance would ultimately lead to more personalized recommendations and responses (Dwivedi et al., 2021).”
Dwivedi, Y. K., Kshetri, N., Hughes, L., Slade, E. L., Jeyaraj, A., Kar, A. K., Baabdullah, A. M., Koohang, A., Raghavan, V., Ahuja, M., et al. (2023) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2023.102642 · Cited by 4,584
Training language models to follow instructions with human feedback
Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C. L., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al. (2022) · arXiv · doi:10.48550/arxiv.2203.02155 · Cited by 4,208 · cited 1×
“To enhance its comprehension of user queries and generate accurate responses, ChatGPT undergoes continuous refinement using reinforcement methods, machine learning, and natural language processing techniques (Ouyang et al., 2022).”
Kung, T. H., Cheatham, M., Medenilla, A., Sillos, C., De Leon, L., Elepaño, C., Madriaga, M., Aggabao, R., Diaz-Candido, G., Maningo, J., et al. (2023) · PLOS Digital Health · doi:10.1371/journal.pdig.0000198 · Cited by 4,115 · cited 2×
“With the aid of this information, ChatGPT will be better able to provide customers in the medical industry with personalized answers by better comprehending the linguistic conventions and terminologies used there (Eysenbach et al., 2023; Kung et al., 2023).”
Research works printed but not linked: 34
- Abdel-Messih, 2023, ‘Chatgpt in clinical toxicology’, JMIR, Med. Educ., 9
- Aljanabi, 2023, Chatgpt: Open possibilities, Iraqi J. Comput. Sci. Mathe., 4, 62
- Arif, T.B., Munaf, U., Ul-Haque, I., 2023b. The future of medical education and research: Is chatgpt a blessing or blight in disguise?. · DOI · Paper page
- Cao, Y., Li, S., Liu, Y., Yan, Z., Dai, Y., Yu, P.S., Sun, L., 2023. A comprehensive survey of ai-generated content (aigc): A history of generative ai from gan to chatgpt, arXiv preprint arXiv:2303.04226. · arXiv
- Cascella, 2023, Evaluating the feasibility of chatgpt in healthcare: An analysis of multiple clinical and research scenarios, J. Med. Syst., 47, 10.1007/s10916-023-01925-4 · DOI · Paper page
- Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P. d. O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G. et al., 2021. Evaluating large language models trained on code, arXiv preprint arXiv:2107.03374. · arXiv
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- Dönmez, İ., Sahin, I., Gülen, S., 2023. Conducting academic research with the ai interface chatgpt: Challenges and opportunities. J. STEAM Educ. 6(2), 101–118.
- Editorialge, Jan, 2023. Elsevier and Cambridge university allow use of chatgpt for academic writing, https://editorialge.com/elsevier-and-cambridge-university-allow-use-chatgpt/. [Online; accessed 29-March-2023].
- Fijacko, 2023, Can chatgpt pass the life support exams without entering the american heart association course?, Resuscitation, 185, 10.1016/j.resuscitation.2023.109732 · DOI · Paper page
- Gao, M., Ruan, J., Sun, R., Yin, X., Yang, S., Wan, X., 2023. Human-like summarization evaluation with chatgpt, arXiv preprint arXiv:2304.02554. · arXiv
- Gaševic, 2023, ‘Empowering learners for the age of artificial intelligence’, Computers and Education, Artif. Intell.
- Gilson, 2023, How does chatgpt perform on the united states medical licensing examination? the implications of large language models for medical education and knowledge assessment, JMIR Med. Educ., 9, e45312, 10.2196/45312 · DOI · Paper page
- Hendy, A., Abdelrehim, M., Sharaf, A., Raunak, V., Gabr, M., Matsushita, H., Kim, Y.J., Afify, M., Awadalla, H.H., 2023. How good are gpt models at machine translation? a comprehensive evaluation, arXiv preprint arXiv:2302.09210. · arXiv
- Jungwirth, 2023, Artificial intelligence and public health: An exploratory study, Int. J. Environ. Res. Public Health, 20, 4541, 10.3390/ijerph20054541 · DOI
- Kasai, J., Kasai, Y., Sakaguchi, K., Yamada, Y., Radev, D., 2023. Evaluating gpt-4 and chatgpt on japanese medical licensing examinations, arXiv preprint arXiv:2303.18027. · arXiv
- Lamichhane, B., 2023. Evaluation of chatgpt for nlp-based mental health applications, arXiv preprint arXiv:2303.15727. · arXiv
- Lee, 2023, The rise of chatgpt: Exploring its potential in medical education, Anatom. Sci. Educ.
- Mann, 2023, Artificial intelligence discusses the role of artificial intelligence in translational medicine: A jacc: Basic to translational science interview with chatgpt, JACC: Basic Translat. Sci., 8, 221
- Melis, G., Dyer, C., Blunsom, P., 2017. On the state of the art of evaluation in neural language models, arXiv preprint arXiv:1707.05589. · arXiv
- Mitrovic, S., Andreoletti, D., Ayoub, O., 2023. Chatgpt or human? detect and explain. explaining decisions of machine learning model for detecting short chatgpt-generated text, arXiv preprint arXiv:2301.13852. · arXiv
- Ortega-Martín, M., García-Sierra, Ó., Ardoiz, A., Álvarez, J., Armenteros, J.C., Alonso, A., 2023. Linguistic ambiguity analysis in chatgpt, arXiv preprint arXiv:2302.06426. · arXiv
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Shormani, M. Q. (2025) · Artificial Intelligence Review · doi:10.1007/s10462-025-11332-5 · Cited by 20
“Its first version is GPT-1, having 117 million parameters, and has been trained on massive amounts of data (Ernst and Bavota 2022; Sohail et al. 2023).”
InfluentialRepresentations in Developing Statistical Thinking in Secondary Education
Selvakumar, P., Sharma, A., Roy, S. S., Mohit, M., & Manjunath, T. C. (2025) · IGI Global eBooks · doi:10.4018/979-8-3693-9934-7.ch005 · Cited by 5
Hamidi, H. (2025) · Telematics and Informatics Reports · doi:10.1016/j.teler.2025.100237 · Cited by 4
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Self-citationChatGPT and Generative AI in Educational Ecosystems
Haidar, A. (2024) · Advances in educational marketing, administration, and leadership book series · doi:10.4018/979-8-3693-1536-1.ch004 · Cited by 4
On Combining the Potential of Social Robots and ChatGPT for Enhanced Learning
Jamil, N., Saghir, F. N., Alshanqiti, H., Almansoori, A. K. A., Saeed, A., Ahmad, A., & Belkacem, A. N. (2024) · International Conference on Information and Education Technology (ICIET) · doi:10.1109/iciet60671.2024.10542738 · Cited by 5
“…physical presence and the social capabilities of ChatGPT’s diverse conversational abilities, educators can establish a dynamic and adaptable learning environment to effectively address the unique needs of individual students, promote active participation, and mitigate educational disparities [1].”
BackgroundCurrent Topological and Machine Learning Applications for Bias Detection in Text
Farrelly, C., Singh, Y., Hathaway, Q. A., Carlsson, G., Choudhary, A., Paul, R., Doretto, G., Himeur, Y., Atalls, S., & Mansoor, W. (2023) · International Conference on Signal Processing and Information Security (ICSPIS) · doi:10.1109/icspis60075.2023.10343824 · Cited by 6
“Typically, a block of text is first parsed into individual words via a process called tokenization [27], [28].”
BackgroundSelf-citationApplication of ChatGPT in seepage-induced slope stability
Kwak, J., & Won, J. (2025) · KSCE Journal of Civil Engineering · doi:10.1016/j.kscej.2025.100457 · Cited by 3
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Mapping the complexity of learners’ attitudes toward ChatGPT: preliminary validation of a new scale
Zhang, Y.-C., Xue, X., Ding, M., & Yang, X.-M. (2025) · Behavior and Information Technology · doi:10.1080/0144929x.2025.2520595 · Cited by 2
“…thorough literature review (Acosta-Enriquez et al. 2024; Chan and Hu 2023; Chen, Hu, and Wang 2024; Granic and Marangunic 2019; Koverola et al. 2022; Rawas 2024; Schepman and Rodway 2020, 2023; Sohail et al. 2023; Strzelecki 2023; Tamilmani et al. 2021; Wang 2024; Yilmaz, Yilmaz, and Ceylan 2023).”
Deep learning accelerated numerical simulation for three-dimensional compressible fluids
Zhang, M., Cui, Q., Xiao, M., & Ni, G. (2024) · Physics of Fluids · doi:10.1063/5.0214669 · Cited by 4
ChatGPT and its Double-Edged Impact on Higher Education and Research- A Detailed Survey
Jagadeesh, M., Aneesh Saikrishna, P., Ali, A. S., & Athish, B. S. (2023) · International Conference on Recent Trends in Advance Computing (ICRTAC) · doi:10.1109/icrtac59277.2023.10480767 · Cited by 3
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Linguistic profile of LLMs: an overview of linguistic performance and jailbreak vulnerability
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Digital “Dialogues” on Sustainability: Stakeholder Alignment in Italian Coastal Tourism
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ChatGPT charting the course: Artificial intelligence in healthcare—Insights and future prospects
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Self-citationIn-Depth Outlook on the Use of ChatGPT
Dhankhar, S. (2024) · Advances in business information systems and analytics book series · doi:10.4018/979-8-3693-0815-8.ch009 · Cited by 2
Use ChatGPT to Inspect Software Developed by Novice Programmers
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Leveraging ChatGPT-4 for Enhanced Education: Personalized Problem-Solving and Consistent Learning
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“This versatility makes it an ideal candidate for diverse linguistic tasks across various academic disciplines[32].”
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Enhancing Breast Imaging Strategies: The Role of ChatGPT in Optimizing Screening Pathways
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What is the AIGC-assisted learning experience like? -- A study based on conceptual understanding
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“The AIGC based on explicit input provided by the user or through user’s implicit interaction with the system dynamically adjusts the strategy for content generation, while understanding and utilizing contextual information to generate personalized content[ 6 ].”
Teufelberger, L., Liu, X., Li, Z., Moebus, M., & Holz, C. (2024) · ACM Symposium on User Interface Software and Technology (UIST) · doi:10.1145/3672539.3686757 · Cited by 1
“With the prolifc progress in open-source NLP research, there are many studies on specialized bots and apps for specifc use cases such as domain-specifc or task-orientated text generation [8, 11].”
A Roadmap for the Future Research of the White Stork Ciconia ciconia Population in Poland
Tryjanowski, P., Białas, J. T., Jankowiak, Ł., Jerzak, L., Mielczarek, P., Nowak, M. K., Profus, P., Siekiera, J., Tobółka, M., Walasz, K., et al. (2024) · Polish Journal of Ecology · doi:10.3161/15052249pje2024.72.1.004 · Cited by 1
“Owing to the opaque nature of the neural network (black box), it is challenging to decipher GPT’s workings, thus complicating the discussion of the results generated (Sohail et al. 2023).”
ChatGPT and Conversational AI in Customer Satisfaction: A Preliminary Literature Review
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Exploring the Merchera Ethnic Group Through ChatGPT: The Risks of Epistemic Exclusion
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“While such tools may facilitate preliminary explorations, their content must be rigorously cross-validated using established academic practices [59].”
Guo, Y., & Shi, L. (2024) · International Conference on Information Science and Education (ICISE-IE) · doi:10.1109/icise-ie64355.2024.11025507 · Cited by 1
“A literature review on ChatGPT, one of the AIGC tools, found that researchers currently focus on predicting the future of topics in the context of AIGC and evaluating the functions of AIGC tools and their potential impact [2].”
Human-Robot Collaboration with Large Language Model for Model Construction Using Complex Software
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Using machine learning for early identification of At-risk students
Ewais, A., Arram, A. W., Diab, S., Alkhazaleh, H. A., Himeur, Y., & Ashqar, H. I. (2026) · Scientific Reports · doi:10.1038/s41598-026-48426-1 · Cited by 1
Self-citationAlzahrani, S., & Bhunia, A. K. (2026) · Frontiers in Education · doi:10.3389/feduc.2026.1803390 · Cited by 1
Addressing obesity and homelessness via ChatGPT
Sohail, S. S. (2023) · Clinical Medicine · doi:10.7861/clinmed.let.23.6.3 · Cited by 1
Self-citationDetecting Wasted Resources in Hydroponic Systems
Almadhaani, R. Y., Albeloshi, K. A., AlAnsaari, M., Alkhazaleh, H. A., Amin, S. A., & Atalla, S. (2023) · International Conference on Signal Processing and Information Security (ICSPIS) · doi:10.1109/icspis60075.2023.10343790 · Cited by 1
“This step will help us understand the data ranges and if data transformation is necessary [21, 22]. b) Correlation Matrix: Since we have a lot of variables, we need to understand the correlation between the variables and how each one affects the other.”
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Innovations With ChatGPT in the Tourism Industry
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Ayad, T. H., & abouelenien, R. (2024) · Journal of Infrastructure Policy and Development · doi:10.24294/jipd.v8i6.4183 · Cited by 1
“According to Sohail et al. (2023), Wang et al. (2023), and Faruk et al. (2023), it can replicate human-like text production, understand complicated contexts, express emotions, and employ sarcasm.”
BackgroundDesign of a Web-Based Information System for New Student Registration in Vocational High Schools
Iskandar, A., Retnawati, H., Haryanto, H., & Sahariani (2024) · Ingénierie des systèmes d information · doi:10.18280/isi.290420 · Cited by 1
“A system has an important role in identifying relevant information input and producing appropriate output as a result [12].”
Islam, M. N., & Aziz, M. T. B. (2024) · Lecture notes in electrical engineering · doi:10.1007/978-981-97-8031-0_37 · Cited by 1
The use of ChatGPT in the workplace: a bibliometric analysis of integration and influence trends
Obrenovic, B., Romeo, A., & Oblaković, G. (2025) · AI & Society · doi:10.1007/s00146-025-02447-y · Cited by 1
A systematic review on the role of ChatGPT on student learning
Iqbal, J. (2024) · Integration of Mankind and Society · doi:10.56770/ims2024123 · Cited by 1
Are Horses Always Strong and Donkeys Dumb? Animal Bias in Vision Language Models
Anas, M., Nadeem, M., Sohail, S. S., Cambria, E., & Hussain, A. (2025) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn64981.2025.11228584 · Cited by 1
“The applications of VLMs are extensive and span across various domains, including image captioning [7], [8], visual question answering [9], [10] and image generation [11], [12].”
Self-citationNag, P. K., Bhagat, A., & Priya, R. V. (2024) · doi:10.36227/techrxiv.172226053.36460987/v1 · Cited by 1
ChatGPT in ultrasound medicine: potential applications and challenges (Preprint)Preprint
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Opportunities and Challenges of Big Data Technology in Retail Industry
Alraees, R. M., Abduljalil, M., Majid, N., Alkhazaleh, H. A., Atalla, S., & Amin, S. A. (2023) · International Conference on Signal Processing and Information Security (ICSPIS) · doi:10.1109/icspis60075.2023.10344206 · Cited by 1
Self-citationHow Do Information Technology Professionals Use Generative Artificial Intelligence?
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Chatgpt-Assisted Decision-Making: An In-Depth Exploration of the Human-Ai InteractionPreprint
Mustata, M. A., Iliescu, D., Mavriș, E., Jude, C.-R., Bojor, L., Tudorache, P., Cîrdei, A., Hrab, D.-E., Covaci, B., Răpan, I., et al. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4870176
Barreto, I. B. L. R., & Vilca, Y. H. (2024) · Lecture notes in networks and systems · doi:10.1007/978-981-97-3289-0_33 · Cited by 1
Chat Generative Pre-Trained Transformer (ChatGPT): Boon or Bane – A Qualitative Meta-Synthesis
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Performance Evaluation of LLMs with Deep Learning Models for Fake News Detection
Parvez, M. Z., Dawar, S. M., Khan, M. U., & Nadeem, M. (2024) · Lecture notes in networks and systems · doi:10.1007/978-981-97-6103-6_39
Self-citationMulMed: Addressing Multiple Medical Tasks Utilizing Large Language ModelsPreprint
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Thoughts on the Construction of Art Curriculum Using Generative AI
Qiao, R., Zhao, Y., & Han, Z. (2024) · Journal of Educational Research and Policies · doi:10.53469/jerp.2024.06(10).11
“Since the advent of ChatGPT in November 2022, research related to generative AI has been in the exploratory stage in the field of art education [1].”
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An example of using ChatGPT for football recruitment purposes
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Large Language Models for Corporate Financial Distress Prediction: Overview and Exploration
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AI-driven fuzzy requirement engineering for cybersecurity: integrating linguistic decision models
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Can’t Say Cant? Measuring and Reasoning of Dark Jargons in Large Language Models
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Research on AIGC: Empowered Strategies for Film and Television Production
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Designing a Structured Educational Data for AI: A Dataset for Outcome-Based Learning and Assessment
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Image to Text and Text to Image Generation: Artificial Intelligence in Clothing Design
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Deep Learning for Building Maintenance Optimization
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Metadata
- Authors
- Sohail, S. S., Farhat, F., Himeur, Y., Nadeem, M., Madsen, D. Ø., Singh, Y., Atalla, S., & Mansoor, W.
- Affiliations
- Jamia Hamdard (IN)
- Aligarh Muslim University (IN)
- University of Dubai (AE)
- University of South-Eastern Norway (NO)
- Mayo Clinic (US)
- Corresponding author
- Sohail, Shahab Saquib
- Farhat, Faiza
- Himeur, Yassine
- Nadeem, Mohammad
- Madsen, Dag Øivind
- Singh, Yashbir
- Atalla, Shadi
- Mansoor, Wathiq
- ORCID
- Venue
- Journal of King Saud University - Computer and Information Sciences
- Published
- 2023-09
- Volume
- 35
- Issue
- 8
- Pages
- 101675
- ISSN
- 1319-1578 (ISSN-L)
- OpenAlex
- W4385494594
- Semantic Scholar
- 4f4058c6c0333a5bab7eba59a4b28bd7a8b51620
- Cited by
- 192
- References
- 201
- Citations by year
- 2026: 31 · 2025: 58 · 2024: 84 · 2023: 19
- FWCI
- 18.02
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Journal of King Saud University - Computer and Information Sciences (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion, cc-by)
- NORA - Norwegian Open Research Archives (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- arXiv (Cornell University) (cc-by)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Keywords
- ChatGPT, large language models, research taxonomy, algorithmic bias, trustworthiness, conversational AI, health care, financial services, software engineering, academic writing, natural language processing
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
A language model drew the claims from the paper on 2026-10-06, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-10-06 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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