The benefits and challenges of chatgpt: An overview
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This paper provides an overview of ChatGPT, a natural language processing (NLP) system developed by Open AI. It discusses the features of ChatGPT, its benefits, and its challenges. The paper also provides an analysis of the potential applications of ChatGPT and its limitations. The paper concludes that ChatGPT is a powerful NLP system that can generate human-like conversations, but it has some challenges that must be addressed.
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ChatGPT, among other LLMs, can be beneficial in conversational and writing tasks, assisting to increase the efficiency and accuracy of the required output.
“On one hand, ChatGPT, among other LLMs, can be beneficial in conversational and writing tasks, assisting to increase the efficiency and accuracy of the required output [11].”
From ChatGPT Utility in Healthcare Education, Research, and Practice: Systematic Review on the Promising Perspectives and Valid Concerns (2023) · cites this paper as [11]
Sections 4.1 (Increased Efficiency) and 4.2 (Improved Accuracy) say ChatGPT can make conversations more efficient and generate more accurate responses. The paper does not establish those benefits for writing tasks or for other LLMs, so the broader claim is only partially supported. (LLM, conf=0.88)
Experimental — accuracy not yet measuredConcerns have been raised that possible bias based on the datasets used in ChatGPT training can limit its capabilities and could result in factual inaccuracies that appear scientifically plausible (hallucination).
“On the other hand, concerns have been raised in relation to possible bias based on the datasets used in ChatGPT training, which can limit its capabilities and could result in factual inaccuracies, but alarmingly appear to be scientifically plausible (a phenomenon termed hallucination) [11].”
From ChatGPT Utility in Healthcare Education, Research, and Practice: Systematic Review on the Promising Perspectives and Valid Concerns (2023) · cites this paper as [11]
Section 5.2, “Limited Capabilities” (page unavailable), says ChatGPT’s training dataset may lead to biased responses and that it may provide inaccurate information. It does not say that dataset bias causes factual inaccuracies, or that those inaccuracies appear scientifically plausible; the retrieved passages do not describe hallucination. (LLM, conf=0.86)
Experimental — accuracy not yet measuredSecurity concerns and the potential of cyber-attacks with the spread of misinformation utilizing LLMs should be considered.
“Additionally, security concerns and the potential of cyber-attacks with the spread of misinformation utilizing LLMs should also be considered [11].”
From ChatGPT Utility in Healthcare Education, Research, and Practice: Systematic Review on the Promising Perspectives and Valid Concerns (2023) · cites this paper as [11]
ChatGPT might hallucinate, i.e., create new data or information which does not exist.
“ChatGPT might hallucinate i.e., create new data/information which does not exist Deng & Lin (2022).”
From Decoding ChatGPT: A taxonomy of existing research, current challenges, and possible future directions (2023) · cites this paper as Deng & Lin (2022)
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(2023) · Journal of Pharmaceutical Policy and Practice · doi:10.1186/s40545-023-00624-2 · Cited by 42 · see citing works
Deceptive AI Ecosystems: The Case of ChatGPT
Zhan, X., Xu, Y., & Sarkadi, Ş. (2023) · ACM Conference on Conversational User Interfaces (CUI) · doi:10.1145/3571884.3603754 · Cited by 45 · see citing works
Cassinadri, G. (2024) · Philosophy & Technology · doi:10.1007/s13347-024-00701-7 · Cited by 41 · see citing works
“…the huge variety of AI applications in educational settings, one interesting AI tool that can be employed for educational purposes is ChatGPT, which is an AI chatbot (developed by OpenAI) capable of producing suitable responses and engaging in natural-sounding conversations (Deng & Lin, 2023).”
BackgroundA New Approach to Web Application Security: Utilizing GPT Language Models for Source Code Inspection
Szabó, Z., & Bilicki, V. (2023) · Future Internet · doi:10.3390/fi15100326 · Cited by 40 · see citing works
ChatGPT: a game changer for knowledge management in organizations
Sumbal, M. S., & Amber, Q. (2024) · Kybernetes · doi:10.1108/k-06-2023-1126 · Cited by 36 · see citing works
Korucu-Kış, S. (2024) · Thinking Skills and Creativity · doi:10.1016/j.tsc.2024.101639 · Cited by 37 · see citing works
Under the Influence: A Survey of Large Language Models in Fake News Detection
Kuntur, S., Wróblewska, A., Paprzycki, M., & Ganzha, M. (2024) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2024.3471735 · Cited by 43 · see citing works
Talking technology: exploring chatbots as a tool for cataract patient education
Yılmaz, İ. E., & Doğan, L. (2024) · Clinical and Experimental Optometry · doi:10.1080/08164622.2023.2298812 · Cited by 39 · see citing works
ChatGPT: A Conceptual Review of Applications and Utility in the Field of Medicine
Rao, S. J., Isath, A., Krishnan, P., Tangsrivimol, J. A., Virk, H. U. H., Wang, Z., Glicksberg, B. S., & Krittanawong, C. (2024) · Journal of Medical Systems · doi:10.1007/s10916-024-02075-x · Cited by 35 · see citing works
- Authors
- Deng, J., & Lin, Y.
- Affiliations
- City University of Macau (MO)
- ORCID
- Lin, Yijia
- Venue
- Frontiers in Computing and Intelligent Systems
- Publisher
- Darcy & Roy Press Co. Ltd.
- Published
- 2023-01-05
- Volume
- 2
- Issue
- 2
- Pages
- 81-83
- ISSN
- 2832-6024 (ISSN-L)
- OpenAlex
- W4315797044
- Cited by
- 611
- References
- 4
- Citations by year
- 2026: 78 · 2025: 139 · 2024: 237 · 2023: 155 · 2022: 1
- FWCI
- 57.25
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Locations
- Frontiers in Computing and Intelligent Systems (publishedVersion, cc-by)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Speech and dialogue systems
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Keywords
- ChatGPT, large language models, natural language processing, chatbots, OpenAI
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.
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.
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