all AI news
Unlocking Multi-View Insights in Knowledge-Dense Retrieval-Augmented Generation
April 22, 2024, 4:46 a.m. | Guanhua Chen, Wenhan Yu, Lei Sha
cs.CL updates on arXiv.org arxiv.org
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
More from arxiv.org / cs.CL updates on arXiv.org
Jobs in AI, ML, Big Data
Data Engineer
@ Lemon.io | Remote: Europe, LATAM, Canada, UK, Asia, Oceania
Artificial Intelligence – Bioinformatic Expert
@ University of Texas Medical Branch | Galveston, TX
Lead Developer (AI)
@ Cere Network | San Francisco, US
Research Engineer
@ Allora Labs | Remote
Ecosystem Manager
@ Allora Labs | Remote
Founding AI Engineer, Agents
@ Occam AI | New York