AI RESEARCH
Guaranteeing Knowledge Integration with Joint Decoding for Retrieval-Augmented Generation
arXiv CS.CL
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ArXi:2604.08046v1 Announce Type: new Retrieval-Augmented Generation (RAG) significantly enhances Large Language Models (LLMs) by providing access to external knowledge. However, current research primarily focuses on retrieval quality, often overlooking the critical ''integration bottleneck'': even when relevant documents are retrieved, LLMs frequently fail to utilize them effectively due to conflicts with their internal parametric knowledge. In this paper, we argue that implicitly resolving this conflict in a single generation pass is suboptimal. We