RAG遇见LLM综述：迈向检索增强的大语言模型
A Survey on RAG Meeting LLMs: Towards Retrieval-Augmented Large Language Models
AI总结：
本文综述检索增强大语言模型RA-LLMs，从架构、训练策略与应用系统梳理RAG如何利用外部权威知识缓解LLM幻觉和知识过时问题，并指出局限与未来方向。
摘要：
作为人工智能领域最先进的技术之一，检索增强生成（Retrieval-Augmented Generation，RAG）能够提供可靠且最新的外部知识，为众多任务提供了极大便利。特别是在人工智能生成内容（AI-Generated Content，AIGC）时代，检索在提供额外知识方面的强大能力，使RAG能够辅助现有生成式AI产出高质量结果。近年来，大语言模型（Large Language Models，LLMs）在语言理解和生成方面展现出革命性能力，但仍面临幻觉和内部知识过时等固有局限。鉴于RAG在提供最新且有用的辅助信息方面的强大能力，检索增强大语言模型（Retrieval-Augmented Large Language Models，RA-LLMs）应运而生，其利用外部权威知识库而非仅依赖模型内部知识，以提升LLMs的生成质量。在本综述中，我们全面回顾了RA-LLMs的现有研究，涵盖三大主要技术视角：架构、训练策略和应用。作为预备知识，我们简要介绍LLMs的基础与最新进展。随后，为阐明RAG对LLMs的现实意义，我们按架构、训练策略和应用领域系统梳理主流相关工作，具体详述各方面挑战以及RA-LLMs相应能力。最后，为提供更深入洞见，我们讨论当前局限和若干有前景的未来研究方向。本综述的更新信息可参见 https://advanced-recommender-systems.github.io/RAG-Meets-LLMs/ 。
英文摘要：
As one of the most advanced techniques in AI, Retrieval-Augmented Generation (RAG) can offer reliable and up-to-date external knowledge, providing huge convenience for numerous tasks. Particularly in the era of AI-Generated Content (AIGC), the powerful capacity of retrieval in providing additional knowledge enables RAG to assist existing generative AI in producing high-quality outputs. Recently, Large Language Models (LLMs) have demonstrated revolutionary abilities in language understanding and generation, while still facing inherent limitations, such as hallucinations and out-of-date internal knowledge. Given the powerful abilities of RAG in providing the latest and helpful auxiliary information, Retrieval-Augmented Large Language Models (RA-LLMs) have emerged to harness external and authoritative knowledge bases, rather than solely relying on the model's internal knowledge, to augment the generation quality of LLMs. In this survey, we comprehensively review existing research studies in RA-LLMs, covering three primary technical perspectives: architectures, training strategies, and applications. As the preliminary knowledge, we briefly introduce the foundations and recent advances of LLMs. Then, to illustrate the practical significance of RAG for LLMs, we systematically review mainstream relevant work by their architectures, training strategies, and application areas, detailing specifically the challenges of each and the corresponding capabilities of RA-LLMs. Finally, to deliver deeper insights, we discuss current limitations and several promising directions for future research. Updated information about this survey can be found at https://advanced-recommender-systems.github.io/RAG-Meets-LLMs/
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。
