Retrieval-Augmented Generation (RAG) merges retrieval methods with deep learning advancements to address the static limitations of large language models (LLMs) by enabling the dynamic integration of up-to-date external information. This methodology, focusing primarily on the text domain, provides a cost-effective solution to the generation of plausible but incorrect responses by LLMs, thereby enhancing the accuracy and reliability of their outputs through the use of real-world data. As RAG grows in complexity and incorporates multiple concepts that can influence its performance, this paper organizes the RAG paradigm into four categories: pre-retrieval, retrieval, post-retrieval, and generation, offering a detailed perspective from the retrieval viewpoint. It outlines RAG's evolution and discusses the field's progression through the analysis of significant studies. Additionally, the paper introduces evaluation methods for RAG, addressing the challenges faced and proposing future research directions. By offering an organized framework and categorization, the study aims to consolidate existing research on RAG, clarify its technological underpinnings, and highlight its potential to broaden the adaptability and applications of LLMs.

翻译：检索增强生成（Retrieval-Augmented Generation, RAG）将检索方法与深度学习进展相结合，通过动态整合最新外部信息，突破了大语言模型（LLMs）的静态局限性。该方法主要聚焦于文本领域，为LLMs生成看似合理但错误的响应提供了一种经济高效的解决方案，从而借助真实世界数据提升其输出的准确性与可靠性。随着RAG日益复杂并融合多个可能影响其性能的概念，本文从检索视角将RAG范式归为四类：预检索、检索、后检索与生成，并提供了详细阐述。文章概述了RAG的演进历程，并通过分析重要研究成果探讨了该领域的发展进程。此外，本文还介绍了RAG的评估方法，讨论了当前面临的挑战，并提出了未来研究方向。通过构建系统化的框架与分类体系，本研究旨在整合现有RAG研究，阐明其技术基础，并突出其在拓展LLMs适应性与应用潜力方面的价值。