Summary of ChatGPT-Related Research and Perspective Towards the Future of Large Language Models

By Yiheng Liu, Tianle Han, Siyuan Ma, Jia-Yu Zhang, Yuanyu Yang, Jiaming Tian, Haoyang He, Antong Li, Mengshen He, Zheng Liu and 7 more. Meta-Radiology, 2023

745 citations recorded by the source corpus.

This paper presents a comprehensive survey of ChatGPT-related (GPT-3.5 and GPT-4) research, state-of-the-art large language models (LLM) from the GPT series, and their prospective applications across diverse domains. Indeed, key innovations such as large-scale pre-training that captures knowledge across the entire world wide web, instruction fine-tuning and Reinforcement Learning from Human Feedback (RLHF) have played significant roles in enhancing LLMs' adaptability and performance. We performed an in-depth analysis of 194 relevant papers on arXiv, encompassing trend analysis, word cloud representation, and distribution analysis across various application domains. The findings reveal a significant and increasing interest in ChatGPT-related research, predominantly centered on direct natural language processing applications, while also demonstrating considerable potential in areas ranging from education and history to mathematics, medicine, and physics. This study endeavors to furnish insights into ChatGPT's capabilities, potential implications, ethical concerns, and offer direction for future advancements in this field.

Subfields: Artificial Intelligence, Health Informatics

View Paper: https://www.semanticscholar.org/paper/51a0bba0c5fb4257e843040615bb23f712fed4e6

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Citations in the corpus

GPT (Generative Pre-Trained Transformer)— A Comprehensive Review on Enabling Technologies, Potential Applications, Emerging Challenges, and Future Directions (2023, methodology): “The language barriers in using GPT must be lessened to improve user experience and utilize the features of GPT [112].”
Voyager: An Open-Ended Embodied Agent with Large Language Models (2023, background): “This finding corroborates recent studies in the literature [57, 58].”
A Survey of Knowledge Graph Reasoning on Graph Types: Static, Dynamic, and Multi-Modal (2022, background): “Large language models (LLMs) [317], i.e., ChatGPT, GPT-4, are very popular this year, which have huge impacts due to their promising reasoning capacity and generalizability [318], [319].”