Summary Of Chatgpt-related Research And Perspective Towards The Future Of Large Language Models

Liu Yiheng, Han Tianle, Ma Siyuan, Zhang Jiayue, Yang Yuanyuan, Tian Jiaming, He Hao, Li Antong, He Mengshen, Liu Zhengliang, Wu Zihao, Zhao Lin, Zhu Dajiang, Li Xiang, Qiang Ning, Shen Dingang, Liu Tianming, Ge Bao. Meta-Radiology 2023

[Paper]    
Agentic Applications Fine Tuning GPT Model Architecture Pretraining Methods Reinforcement Learning Survey Paper Training Techniques

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.

Similar Work