Abstract
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.
Abstract (translated)
虽然Retrieval-Augmented Generation（RAG）在大型语言模型的应用中扮演着关键角色，但知识密集领域（如法律和医学）中的现有检索方法仍然存在多视角视图的缺乏，这些多视角视图对提高可解释性和可靠性至关重要。先前关于多视角检索的研究通常仅关注不同语义形式的查询，而忽视了特定领域知识视角的表达。本文介绍了一种名为MVRAG的新多视角检索框架，专门针对知识密集领域，利用多个领域视角的意图感知查询重写来提高检索精度，从而提高最终推理的有效性。在法律和医学案例检索实验中，我们的框架显示出显著的召回和精确率提高。我们的多视角检索方法释放了多视角信息增强RAG任务的潜力，加速了LLM在知识密集领域进一步应用。
URL
https://arxiv.org/abs/2404.12879
