Large language models (LLMs) have achieved strong empirical performance in various fields, benefiting from their huge amount of parameters that store knowledge. However, LLMs still suffer from several key issues, such as hallucination problems, knowledge update issues, and lacking domain-specific expertise. The appearance of retrieval-augmented generation (RAG), which leverages an external knowledge base to augment LLMs, mitigates these limitations. This paper presents a systematic review of RAG techniques for natural language processing (NLP), with a focus on retrievers and retrieval fusions. We introduce a novel taxonomy of retrieval fusions, such as query-based, logits-based, latent, and parametric fusion, and provide structured comparisons across accessibility, efficiency, and use cases. The paper further examines RAG applications across diverse NLP tasks, discusses evaluation methodologies and benchmark limitations, and analyzes training paradigms with and without knowledge base updates. Finally, we explore industrial deployment considerations and identify emerging challenges and future directions, including security, efficiency, and graph-based retrieval.

翻译：大语言模型凭借其海量参数存储的知识，在各领域取得了强劲的实证表现。然而，大语言模型仍面临若干关键问题，包括幻觉问题、知识更新问题以及缺乏领域专业知识。检索增强生成技术通过利用外部知识库增强大语言模型，有效缓解了这些局限。本文系统综述了面向自然语言处理的检索增强生成技术，重点聚焦检索器与检索融合方法。我们提出了一种新的检索融合分类体系，涵盖基于查询、基于对数几率、基于隐式与基于参数的融合方法，并从可访问性、效率及使用场景层面进行了结构化对比。文章进一步考察了检索增强生成在多种自然语言处理任务中的应用，探讨了评估方法与基准局限性，分析了含知识库更新与不含知识库更新的训练范式。最后，我们探索了工业部署的考量因素，并指出了安全、效率及基于图的检索等新兴挑战与未来方向。