When Context Misleads: Intent-Guided Decoding for Robust Retrieval-Augmented Generation

Abstract:Retrieval-augmented generation (RAG) improves large language models by grounding generation in external evidence, but it also introduces a source trust problem: retrieved context may be useful, irrelevant, or even misleading. Existing RAG systems often apply a fixed trust policy toward retrieved evidence, which can either over-trust incorrect context or underuse context when the user explicitly asks for context-following behavior. Therefore, we propose Intent-Guided Decoding (IGD), a framework that arbitrates between retrieved context and parametric memory according to user intent. IGD uses answer-level filtering and token-level correction to steer the final decoding trajectory between retrieved context and parametric memory. We evaluate IGD on three faithful QA benchmarks and three factual-conflict benchmarks across five LLMs, IGD substantially improves factual recovery, achieving gains of up to 65.4 percentage points on factual-conflict benchmarks over Direct RAG, while preserving or improving strict context-following behavior, this findings highlight the importance of balancing factuality and faithfulness in RAG.

Computation and Language (cs.CL); Artificial Intelligence (cs.AI)

arXiv:2608.16515 [cs.CL]

(or arXiv:2608.16515v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2608.16515 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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