全面解析ChatGPT架构与应用，揭示其技术优势与社会影响
ChatGPT or A Silent Everywhere Helper: A Survey of Large Language Models
- 系统梳理ChatGPT的模型架构与训练流程
- 对比多类LLM在实际应用中的表现差异
- 适合关注AI落地与伦理风险的研究者参考
大型语言模型（LLMs）已彻底改变自然语言处理（NLP），其中聊天生成预训练变换器（ChatGPT）凭借其先进能力与广泛应用脱颖而出。本综述全面分析了ChatGPT的架构、训练过程与功能特性，探讨其在客户服务、教育、医疗和娱乐等行业的集成应用。通过与其他大模型的对比，凸显其独特特征与性能指标。文中还评估了其在各类基准测试中的表现，并讨论了虚假信息、偏见及数据隐私等潜在风险。此外，本文包含多个图表，概述了讨论背景、核心观点、多种大模型、用于预训练、微调与评估的数据集清单，以及相关应用场景与参考文献。最后，指出未来研究方向与技术演进路径，强调大模型对人工智能与社会发展的深远影响。
原文摘要 · Abstract (English)
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.
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