As one of the most advanced techniques in AI, Retrieval-Augmented Generation (RAG) techniques 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 RAG in providing additional knowledge enables retrieval-augmented generation 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abilities in language understanding and generation, while still facing inherent limitations, such as hallucinations and out-of-date internal knowledge.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 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 retrieval-augmented large language models (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 categorize mainstream relevant work by 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.

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）技术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，RAG中检索能力在提供额外知识方面的强大效能，使得检索增强生成能够辅助现有生成式AI产出高质量输出。近年来，大型语言模型（LLMs）在语言理解与生成方面展现出革命性能力，但仍面临诸如幻觉和内部知识过时等固有局限。鉴于RAG在提供最新且有益的辅助信息方面的强大能力，检索增强的大型语言模型应运而生，通过利用外部权威知识库（而非仅依赖模型内部知识）来提升LLMs的生成质量。本综述全面回顾了检索增强大型语言模型（RA-LLMs）领域的现有研究，涵盖三大核心技术视角：架构、训练策略与应用。作为基础性知识，我们简要介绍了LLMs的基础原理与最新进展。随后，为阐明RAG对LLMs的实践意义，我们按应用领域对主流相关工作进行了分类，详细阐述了各领域面临的挑战及RA-LLMs相应的能力。最后，为提供更深刻的见解，我们探讨了当前局限性及未来研究中的若干有前景的方向。