计算机工程与应用2025，Vol.61Issue(20)：19-35,17.DOI:10.3778/j.issn.1002-8331.2501-0061
检索增强生成技术研究综述
Comprehensive Review of Retrieval-Augmented Generation
摘要
Abstract
Large language models have shown strong capabilities in the field of natural language processing,but still face problems such as hallucinations and lack of domain-specific knowledge.Retrieval-augmented generation(RAG)effectively alleviates some of the problems faced by large language models by utilizing large-scale external knowledge bases to enhance the semantic understanding and generation capabilities of the models,and providing an effective solution for natural lan-guage processing tasks such as open-domain question answering,text summarization,and dialogue systems.This paper comprehensively reviews the key technical advances in retrieval-augmented generation,including the retriever,generator,and the possibility of optimizing each part.In addition,it summarizes the existing retrieval-augmented generation evalua-tion methods and explores the limitations of the current RAG evaluation.Finally,possible future research directions for retrieval-augmented generation are discussed.关键词
检索增强生成(RAG)/大语言模型(LLM)/知识库/信息检索Key words
retrieval-augmented generation(RAG)/large language model(LLM)/knowledge base/information retrieval分类
信息技术与安全科学引用本文复制引用
吴璇,付涛..检索增强生成技术研究综述[J].计算机工程与应用,2025,61(20):19-35,17.基金项目
2025年度云南财经大学研究生创新基金(2025YUFEYC152). （2025YUFEYC152）
