Large Language Models (LLMs) demonstrate significant capabilities but face challenges such as hallucination, outdated knowledge, and non-transparent, untraceable reasoning processes.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 models, 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 the metrics and benchmarks for assessing RAG models, along with the most up-to-date evaluation framework. In conclusion, the paper delineates prospective avenues for research, including the identification of challenges, the expansion of multi-modalities, and the progression of the RAG infrastructure and its ecosystem.

翻译：大型语言模型展现了显著能力，但面临幻觉、知识陈旧以及推理过程不透明且不可追溯等挑战。检索增强生成作为一种有前景的解决方案应运而生，通过引入外部数据库的知识，增强了模型在知识密集型任务中的准确性和可信度，并允许持续的知识更新和领域特定信息的整合。RAG将大型语言模型的内在知识与外部数据库庞大且动态的存储内容协同融合。本综述论文对RAG范式的演进进行了详细审视，涵盖朴素RAG、高级RAG和模块化RAG。论文细致剖析了RAG框架的三元基础，包括检索、生成和增强技术。文章重点强调了这些关键组件中嵌入的最新技术，为理解RAG系统的进展提供了深入见解。此外，本文介绍了评估RAG模型的指标与基准，以及最新的评估框架。最后，论文展望了未来研究方向，包括挑战识别、多模态扩展，以及RAG基础设施及其生态系统的演进。