ChatGPT还是无处不在的沉默助手：大语言模型综述
ChatGPT or A Silent Everywhere Helper: A Survey of Large Language Models
AI总结：
本综述全面分析ChatGPT的架构、训练与功能，梳理其多行业应用，对比其他大语言模型的性能与特性，探讨潜在风险，梳理相关模型、数据集与应用，并指明未来研究方向。
摘要：
大语言模型（Large Language Models, LLMs）革新了自然语言处理（Natural Language Processing, NLP）领域，其中聊天生成预训练Transformer（Chat Generative Pre-trained Transformer, ChatGPT）凭借其先进能力和广泛应用成为代表性范例。本综述对ChatGPT展开全面分析，探究其架构、训练流程与功能特性，考察了它在客服、教育、医疗、娱乐等各行业不同领域的整合应用情况。通过与其他LLMs的对比分析，凸显了ChatGPT的独特特性与性能指标。在基准测试方面，本文检验了ChatGPT与其他LLMs的对比性能，并讨论了错误信息、偏见、数据隐私问题等潜在风险。此外，我们提供了大量图表，梳理了讨论背景、文章核心观点、众多LLM模型、预训练、微调与评估所用数据集的完整清单，以及附有相关参考文献的具体LLM应用案例。最后，我们明确了未来研究方向与技术进展，强调了LLM领域的演进格局及其对人工智能（Artificial Intelligence, AI）与社会的深远影响。
英文摘要：
Large Language Models (LLMs) have revo lutionized natural language processing Natural Language Processing (NLP), with Chat Generative Pre-trained Transformer (ChatGPT) standing out as a notable exampledue to its advanced capabilities and widespread applications. This survey provides a comprehensive analysis of ChatGPT, exploring its architecture, training processes, and functionalities. We examine its integration into various domains across industries such as customer service, education, healthcare, and entertainment. A comparative analysis with other LLMs highlights ChatGPT's unique features and performance metrics. Regarding benchmarks, the paper examines ChatGPT's comparative performance against other LLMs and discusses potential risks such as misinformation, bias, and data privacy concerns. Additionally, we offer a number of figures and tables that outline the backdrop of the discussion, the main ideas of the article, the numerous LLM models, a thorough list of datasets used for pre-training, fine-tuning, and evaluation, as well as particular LLM applications with pertinent references. Finally, we identify future research directions and technological advancements, underscoring the evolving landscape of LLMs and their profound impact on artificial intelligence Artificial Intelligence (AI) and society.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
