While Retrieval-Augmented Generation (RAG) plays a crucial role in the application of Large Language Models (LLMs), existing retrieval methods in knowledge-dense domains like law and medicine still suffer from a lack of multi-perspective views, which are essential for improving interpretability and reliability. Previous research on multi-view retrieval often focused solely on different semantic forms of queries, neglecting the expression of specific domain knowledge perspectives. This paper introduces a novel multi-view RAG framework, MVRAG, tailored for knowledge-dense domains that utilizes intention-aware query rewriting from multiple domain viewpoints to enhance retrieval precision, thereby improving the effectiveness of the final inference. Experiments conducted on legal and medical case retrieval demonstrate significant improvements in recall and precision rates with our framework. Our multi-perspective retrieval approach unleashes the potential of multi-view information enhancing RAG tasks, accelerating the further application of LLMs in knowledge-intensive fields.

翻译：尽管检索增强生成（RAG）在大型语言模型（LLM）的应用中扮演着至关重要的角色，但在法律、医学等知识密集型领域，现有的检索方法仍普遍缺乏多视角分析能力，而这对于提升模型的可解释性与可靠性至关重要。以往关于多视图检索的研究往往仅关注查询的不同语义形式，而忽视了特定领域知识视角的表达。本文提出了一种新颖的、专为知识密集型领域设计的**多视图RAG框架——MVRAG**。该框架利用基于多领域视角的意图感知查询重写技术，以提升检索精度，进而改善最终推理的效果。在法律与医学案例检索任务上进行的实验表明，我们的框架在召回率与精确率方面均取得了显著提升。这种多视角检索方法释放了多视图信息增强RAG任务的潜力，有助于加速LLM在知识密集型领域的进一步应用。