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.

翻译：本文对与ChatGPT（GPT-3.5和GPT-4）相关的研究、GPT系列最先进的大语言模型及其在多个领域的潜在应用进行了全面综述。事实上，大规模预训练（从整个全球互联网捕获知识）、指令微调以及基于人类反馈的强化学习等关键创新在提升大语言模型适应性和性能方面发挥了重要作用。我们对arXiv上194篇相关论文进行了深度分析，包括趋势分析、词云表示以及跨多应用领域的分布分析。研究结果表明，与ChatGPT相关的研究兴趣显著且持续增长，主要集中于直接的自然语言处理应用，同时在从教育、历史到数学、医学和物理学等多个领域也展现出巨大潜力。本研究旨在揭示ChatGPT的能力、潜在影响与伦理问题，并为该领域未来的发展指明方向。