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 language 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 evaluation methods and explores the limitations of the current RAG evaluation. Finally, possible future research directions for retrieval-augmented generation are discussed.

Key words: retrieval-augmented generation (RAG), large language model (LLM), knowledge base, information retrieval

摘要： 大语言模型在自然语言处理领域表现出强大的能力，但依然面临诸如幻觉、缺乏领域特定知识等问题。检索增强生成（retrieval-augmented generation，RAG）利用大规模的外部知识库来增强模型的语义理解和生成能力，有效缓解了大语言模型所面临的部分问题，为开放域问答、文本摘要、对话系统等自然语言处理任务提供了有效的解决方案。将全面综述检索增强生成的关键技术进展，包括检索器、生成器以及各个部分优化的可能性；总结了现有的检索增强生成评估方法，探讨了当前RAG评估的局限性。最后，讨论了检索增强生成未来可能的研究方向。

关键词: 检索增强生成（RAG）, 大语言模型（LLM）, 知识库, 信息检索