A Survey on RAG Meets LLMs: Towards Retrieval-Augmented Large Language Models

Institution: NUS, HKPolyU

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in language understanding and generation, while still facing inherent limitations, such as hallucinations and out-of-date internal knowledge.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

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Q&A

Q：这篇论文试图解决什么问题？

A：这篇论文是关于检索增强型大型语言模型（Retrieval-Augmented Large Language Models，简称RA-LLMs）的综述。它试图解决的问题是大型语言模型（LLMs）在提供信息和生成文本时面临的一些固有限制，例如产生幻觉（hallucinations）和内部知识过时。为了克服这些问题，论文探讨了如何通过检索增强技术（Retrieval-Augmented Generation，简称RAG）来提升LLMs的性能。 具体来说，这篇论文的主要目标包括：

  1. 技术综述：全面回顾现有的RA-LLMs研究，涵盖架构、训练策略和应用三个主要技术视角。
  2. 实际意义：通过应用领域的分类，阐释RAG对LLMs的实用重要性，并详细说明每个领域面临的挑战以及RA-LLMs的相应能力。
  3. 深入洞察：讨论当前RA-LLMs的局限性，并探讨未来研究的几个有前景的方向。
  4. 系统性分析：提供对RA-LLMs的系统性回顾，帮助研究者深入理解这一领域的最新进展。
  5. 未来研究方向：鉴于RA-LLMs研究仍处于早期阶段，论文还提出了一些潜在的研究方向，如开发可信赖的RA-LLMs、多语言RA-LLMs、多模态RA-LLMs，以及提高外部知识库的质量。

总的来说，这篇论文试图通过提供一个全面的RA-LLMs研究视角，来解决LLMs在处理特定任务时可能遇到的信息不准确或过时的问题，并通过结合外部知识源来增强模型的生成质量和适应性。

Q：有哪些相关研究？

A：根据提供的论文内容，以下是一些与检索增强型大型语言模型（RA-LLMs）相关的研究工作：

  1. REALM [42]: 一个利用大型语料库进行检索以增强预训练、微调和推理阶段的模型，以提高回答的准确性。
  2. Fusion-in-Decoder [49]: 从支持文档中检索段落，并将其与问题融合以生成答案，提高了回答的准确性。
  3. Self-RAG [5]: 通过自我反思机制，评估检索信息的有用性和可靠性，显著提高了验证的准确性。