The DOI matches its Crossref record; 104 of the 104 references it cites were found in a registry or an index.
A comprehensive survey of ChatGPT: Advancements, applications, prospects, and challenges
Open accessAbstract
Large Language Models (LLMs) especially when combined with Generative Pre-trained Transformers (GPT) represent a groundbreaking in natural language processing. In particular, ChatGPT, a state-of-the-art conversational language model with a user-friendly interface, has garnered substantial attention owing to its remarkable capability for generating human-like responses across a variety of conversational scenarios. This survey offers an overview of ChatGPT, delving into its inception, evolution, and key technology. We summarize the fundamental principles that underpin ChatGPT, encompassing its introduction in conjunction with GPT and LLMs. We also highlight the specific characteristics of GPT models with details of their impressive language understanding and generation capabilities. We then summarize applications of ChatGPT in a few representative domains. In parallel to the many advantages that ChatGPT can provide, we discuss the limitations and challenges along with potential mitigation strategies. Despite various controversial arguments and ethical concerns, ChatGPT has drawn significant attention from research industries and academia in a very short period. The survey concludes with an envision of promising avenues for future research in the field of ChatGPT. It is worth noting that knowing and addressing the challenges faced by ChatGPT will mount the way for more reliable and trustworthy conversational agents in the years to come.
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“Most prior research in this field speculates on high-level concerns such as inaccuracy and real-world impacts [43, 44, 54, 86, 93]. However, these studies rarely articulate concrete criteria for when and why LLMs should or should not provide professional advice to users.”
From (A)I Am Not a Lawyer, But...: Engaging Legal Experts towards Responsible LLM Policies for Legal Advice (2024) · cites this paper as [54]
Citations
Cites
1 retracted
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Generative Artificial Intelligence in Education: From Deceptive to Disruptive.
Alier, M., García‐Peñalvo, F. J., & Camba, J. D. (2024) · International Journal of Interactive Multimedia and Artificial Intelligence · doi:10.9781/ijimai.2024.02.011 · Cited by 267
“If the training dataset for a particular domain lacks depth or breadth, the responses in that domain might not meet the desired standards [73].”
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Metadata
- Authors
- Nazir, A., & Wang, Z.
- Affiliations
- University of Maryland, Baltimore (US)
- Corresponding author
- Wang, Ze
- ORCID
- Venue
- Meta-Radiology
- Publisher
- Elsevier BV
- Published
- 2023-09
- Volume
- 1
- Issue
- 2
- Pages
- 100022
- ISSN
- 2950-1628 (ISSN-L)
- PMC
- PMC10611551
- OpenAlex
- W4387425757
- Semantic Scholar
- 3cc896b9a9c4951510b6b9f28be930d65b24d29f
- Cited by
- 245
- References
- 103
- Citations by year
- 2026: 47 · 2025: 99 · 2024: 95 · 2023: 4
- FWCI
- 23.10
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- Locations
- Meta-Radiology (publishedVersion, cc-by)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Institutes of Health (R01AG060054, 1UL1TR003098, R01AG070227, R01EB031080-01A1, P41EB029460-01A1)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Speech and dialogue systems
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Keywords
- ChatGPT, large language models, generative pre-trained transformer, chatbots, natural language generation, natural language understanding, ethical considerations, trustworthy AI
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