Large Language Models (LLMs) showcase impressive capabilities but encounter challenges like hallucination, outdated knowledge, and non-transparent, untraceable reasoning processes. Retrieval-Augmented Generation (RAG) has emerged as a promising solution by incorporating knowledge from external databases. This enhances the accuracy and credibility of the generation, particularly for knowledge-intensive tasks, and allows for continuous knowledge updates and integration of domain-specific information. RAG synergistically merges LLMs' intrinsic knowledge with the vast, dynamic repositories of external databases. This comprehensive review paper offers a detailed examination of the progression of RAG paradigms, encompassing the Naive RAG, the Advanced RAG, and the Modular RAG. It meticulously scrutinizes the tripartite foundation of RAG frameworks, which includes the retrieval, the generation and the augmentation techniques. The paper highlights the state-of-the-art technologies embedded in each of these critical components, providing a profound understanding of the advancements in RAG systems. Furthermore, this paper introduces up-to-date evaluation framework and benchmark. At the end, this article delineates the challenges currently faced and points out prospective avenues for research and development.

翻译：大语言模型展现出令人瞩目的能力，但仍面临幻觉现象、知识过时以及推理过程不透明且难以追溯等挑战。检索增强生成通过引入外部数据库知识，成为解决上述问题的有效方案。该技术能提升生成内容的准确性与可信度（尤其适用于知识密集型任务），并支持知识的持续更新与领域特定信息的整合。检索增强生成将大语言模型的内隐知识与外部数据库的庞大多样资源进行协同融合。本综述论文系统梳理了检索增强生成范式的演进路径，涵盖朴素检索增强生成、进阶检索增强生成与模块化检索增强生成三个阶段。文章深入剖析了检索增强生成框架的三大核心构成——检索技术、生成技术与增强技术，分别阐明各关键组件所嵌入的最新前沿技术，为理解检索增强生成系统的发展提供深刻洞见。此外，本文介绍了当前评估框架与基准体系，并最终指出现存挑战及未来研发方向。