Deceptive Grounding: Entity Attribution Failure in Clinical Retrieval-Augmented Generation
Retrieval-augmented generation evaluation checks whether model claims are factually grounded in retrieved documents. It does not check whether retrieved evidence is attributed to the correct entity. A clinical RAG response can pass every automated check (zero hallucinations, near-perfect faithfulness, real citations) while presenting drug Y's clinical evidence as evidence about queried drug X. We term this deceptive grounding (DG): a failure invisible to faithfulness, hallucination, and citation checks because every claim is sourced from a real document, about the wrong entity. Using a con
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Paper → model → repo connections mined from source citations (Tier-1 exact match).
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Every edge carries a method, confidence, and the source snippet that justified it — so bad links are debuggable.
- PossiblePossibly related (embedding) · 47%metareflection/claimcheck →
- PossiblePossibly related (embedding) · 46%wanshuiyin/Anti-Autoresearch →
- PossiblePossibly related (embedding) · 46%Can AI help make medical records less biased? New study suggests yes—with caveats - Medical Xpress →
- LinkedLinked via arxiv author · 85%Cedric Caruzzo →
“Deceptive Grounding: Entity Attribution Failure in Clinical Retrieval-Augmented Generation”
- LinkedLinked via arxiv author · 85%Donggeun Yoo →
“Deceptive Grounding: Entity Attribution Failure in Clinical Retrieval-Augmented Generation”
- LinkedLinked via arxiv author · 85%Tae Soo Kim →
“Deceptive Grounding: Entity Attribution Failure in Clinical Retrieval-Augmented Generation”
- PossiblePossibly related (embedding) · 45%Most RAG Hallucinations Are Retrieval Failures: How the Retrieval Brick Decides What the Model Can Invent - Towards Data Science →