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abilities in language understanding and generation, while still facing inherent limitations, such as hallucinations and out-of-date internal knowledge.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/

翻译：作为人工智能领域最先进的技术之一，检索增强生成（RAG）能够提供可靠且最新的外部知识，为众多任务带来极大便利。特别是在人工智能生成内容（AIGC）时代，检索在提供额外知识方面的强大能力使得RAG能够协助现有的生成式AI产出高质量输出。近年来，大语言模型（LLMs）在语言理解与生成方面展现出革命性能力，但仍面临固有局限，例如幻觉问题和过时的内部知识。鉴于RAG在提供最新且有益的辅助信息方面具有强大能力，检索增强大语言模型（RA-LLMs）应运而生，其通过利用外部权威知识库而非仅仅依赖模型内部知识，来增强LLMs的生成质量。本综述全面回顾了RA-LLMs领域的现有研究工作，涵盖三大技术视角：架构、训练策略与应用。作为预备知识，我们简要介绍了LLMs的基础与最新进展。随后，为阐明RAG对于LLMs的实际意义，我们依据架构、训练策略和应用领域对主流相关工作进行了系统梳理，具体详述了每方面面临的挑战以及RA-LLMs相应的解决能力。最后，为提供更深入的见解，我们讨论了当前局限性与未来研究的若干潜在方向。本综述的更新信息可在https://advanced-recommender-systems.github.io/RAG-Meets-LLMs/ 获取。