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A Comprehensive Survey of ChatGPT: Advancements, Applications, Prospects, and Challenges
1Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine. W 670 Baltimore St, HSF III, Room 1173, Baltimore, MD 21201.
This survey explores ChatGPT, a powerful conversational AI based on Large Language Models (LLMs) and Generative Pre-trained Transformers (GPT). It covers ChatGPT's technology, applications, and challenges for future AI development.
Area of Science:
- Artificial Intelligence
- Natural Language Processing
- Machine Learning
Background:
- Large Language Models (LLMs) and Generative Pre-trained Transformers (GPT) represent significant advancements in AI.
- ChatGPT has emerged as a leading conversational AI, notable for its human-like response generation.
- Its user-friendly interface has driven widespread adoption and interest across various sectors.
Purpose of the Study:
- To provide a comprehensive overview of ChatGPT, including its origins and underlying technology.
- To summarize the core principles of ChatGPT, its connection to GPT and LLMs, and GPT models' capabilities.
- To explore ChatGPT's applications, limitations, and potential future research directions.
Main Methods:
- Literature review and synthesis of existing research on Large Language Models (LLMs) and Generative Pre-trained Transformers (GPT).
- Analysis of ChatGPT's architecture, focusing on its language understanding and generation mechanisms.
- Case study approach to summarize representative applications across different domains.
Main Results:
- ChatGPT leverages advanced GPT technology for sophisticated natural language understanding and generation.
- Identified diverse applications of ChatGPT across various fields, showcasing its versatility.
- Documented key limitations and ethical concerns associated with ChatGPT, alongside proposed mitigation strategies.
Conclusions:
- ChatGPT represents a major leap in conversational AI, driven by LLMs and GPT.
- Addressing current challenges is crucial for developing more reliable and trustworthy AI agents.
- Future research should focus on enhancing AI trustworthiness and exploring novel applications.
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