Unlocking Multi-View Insights in Knowledge-Dense Retrieval-Augmented Generation

arXiv:2404.12879v1 Announce Type: new
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 …

abstract application arxiv cs.cl domains improving insights interpretability knowledge language language models large language large language models law llms medicine perspective rag reliability research retrieval retrieval-augmented role type view