Large Language Models (LLMs) demonstrate significant capabilities but face challenges such as hallucination, outdated knowledge, and non-transparent, untraceable reasoning processes.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（LLMs）展现出显著能力，但仍面临幻觉、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（RAG）通过引入外部数据库知识，成为一种有前景的解决方案。该方法提升了模型的准确性和可信度，尤其适用于知识密集型任务，并支持知识的持续更新及领域特定信息的整合。RAG将LLMs的内在知识与外部数据库庞大且动态的存储库协同融合。本综述论文系统梳理了RAG范式的演进历程，涵盖朴素RAG、高级RAG与模块化RAG。论文深入剖析了RAG框架的三元基础，包括检索、生成与增强技术。重点阐述了各关键组件中最前沿的技术突破，为理解RAG系统的进展提供了深刻洞见。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，论文指出了未来研究方向，包括挑战识别、多模态扩展、RAG基础设施及其生态系统的演进。