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 possibly 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（RAG）融合了检索方法与深度学习进展，通过动态整合最新的外部信息，以应对大型语言模型（LLM）的静态局限性。该方法主要聚焦于文本领域，为LLM生成看似合理但可能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复杂性的增加及其所涉及的多个可能影响性能的概念，本文将RAG范式归纳为四个类别：检索前、检索、检索后与生成，并从检索视角提供了详细解析。本文通过梳理重要研究，勾勒了RAG的发展脉络并探讨了该领域的演进过程。此外，论文介绍了RAG的评估方法，分析了当前面临的挑战并提出了未来研究方向。通过提供系统化的框架与分类，本研究旨在整合现有RAG研究，阐明其技术原理，并强调其在拓展LLM适应性与应用范围方面的潜力。