When Context Misleads: Intent-Guided Decoding for Robust Retrieval-Augmented Generation
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 an
Lineage graph
Paper → model → repo connections mined from source citations (Tier-1 exact match).
Why these links exist
Every edge carries a method, confidence, and the source snippet that justified it — so bad links are debuggable.
- FuzzyOverlapping authors or contributors · 62%HKUDS/LightRAG →
“Shared author/contributor keys: jin”
- FuzzyOverlapping authors or contributors · 62%keras-team/keras →
“Shared author/contributor keys: jin”
- LinkedLinked via arxiv author · 85%Haolin Jin →
“When Context Misleads: Intent-Guided Decoding for Robust Retrieval-Augmented Generation”
- LinkedLinked via arxiv author · 85%Pengyue Yang →
“When Context Misleads: Intent-Guided Decoding for Robust Retrieval-Augmented Generation”
- LinkedLinked via arxiv author · 85%Huaming Chen →
“When Context Misleads: Intent-Guided Decoding for Robust Retrieval-Augmented Generation”