  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.
