系统梳理检索增强生成研究，揭示技术现状与未来方向
A Systematic Literature Review of Retrieval-Augmented Generation: Techniques, Metrics, and Challenges
- 按引用量筛选128篇论文，基于PRISMA框架系统分析
- 发现RAG在实时性与泛化间取得平衡，但评估标准不统一
- 适合关注大模型可解释性与知识更新的研究者参考
本系统综述聚焦2020年至2025年5月期间发表的高被引检索增强生成（RAG）研究，共纳入128篇文献。数据来自ACM Digital Library、IEEE Xplore、Scopus、ScienceDirect和DBLP。RAG通过结合神经检索器与生成语言模型，在保留模型权重中语义泛化能力的同时，实现基于非参数记忆的实时输出。依据PRISMA 2020框架，本研究（i）制定基于引用量与研究问题的明确纳入与排除标准；（ii）整理数据集、架构与评估方法；（iii）综合分析RAG的有效性与局限性。为缓解引用滞后偏差，对2025年发表论文采用较低引用阈值，确保捕捉新兴突破。本综述厘清当前研究格局，揭示方法论空白，并提出未来优先研究方向。
原文摘要 · Abstract (English)
This systematic review of the research literature on retrieval-augmented generation (RAG) provides a focused analysis of the most highly cited studies published between 2020 and May 2025. A total of 128 articles met our inclusion criteria. The records were retrieved from ACM Digital Library, IEEE Xplore, Scopus, ScienceDirect, and the Digital Bibliography and Library Project (DBLP). RAG couples a neural retriever with a generative language model, grounding output in up-to-date, non-parametric memory while retaining the semantic generalisation stored in model weights. Guided by the PRISMA 2020 framework, we (i) specify explicit inclusion and exclusion criteria based on citation count and research questions, (ii) catalogue datasets, architectures, and evaluation practices, and (iii) synthesise empirical evidence on the effectiveness and limitations of RAG. To mitigate citation-lag bias, we applied a lower citation-count threshold to papers published in 2025 so that emerging breakthroughs with naturally fewer citations were still captured. This review clarifies the current research landscape, highlights methodological gaps, and charts priority directions for future research.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。