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 controlled factorial benchmark across 13 models, we find DG rates spanning 8-87% at peak adversarial con...
| Authors | Cedric Caruzzo, Donggeun Yoo, Tae Soo Kim |
|---|---|
| Primary category | cs.CL |
| Published | 10 July 2026 |
| Last updated at source | 10 July 2026 |
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