June 11, 2024, 4:41 a.m. | Kiseung Kim, Jay-Yoon Lee

cs.CL updates on arXiv.org arxiv.org

arXiv:2406.05794v1 Announce Type: new
Abstract: Retrieval-augmented generation (RAG) frame work is showing state-of-the-art performance on open-domain question answering tasks by referencing external knowledge. However, the RAG system faces challenges with performance degradation when it is fed contexts of low relevance or when the relative relevance among the input contexts is inaccurately assessed. In this work, we propose a RE-RAG framework that injects an explicit context relevance estimator (RE) into the RAG system. RE-RAG re-evaluates the retrieved contexts with the proposed …

abstract art arxiv challenges cs.ai cs.cl domain estimator fed however improving interpretability knowledge low performance question question answering rag retrieval retrieval-augmented state tasks type work

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