Deceptive Grounding: Entity Attribution Failure in Clinical Retrieval-Augmented Generation

2026-07-10

AI Summary

This study addresses the problem of "deceptive grounding" in clinical retrieval-augmented generation (RAG) systems, wherein models erroneously attribute genuine evidence for one drug to another, producing responses that appear plausible yet are substantively misleading. The work provides the first formal definition and quantitative assessment of this issue, introducing a controlled-factor benchmark to systematically evaluate 13 models across 740 drug–disease pairs. Surprisingly, domain-finetuned models exhibit higher error rates. To mitigate this, the authors propose a high-precision entity attribution verification mechanism combining inverse probability weighting correction with human-curated gold standards, achieving 97.0% precision and 98.7% recall under adversarial conditions. Real-world evaluation reveals an overall deceptive grounding rate of 7.8% in deployed systems—rising to 13.6% for novel drugs—highlighting both the severity of the problem and the efficacy of the proposed solution.

Abstract

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 conditions. Medical and biomedical fine-tuned models reach up to 86.7%; domain specialization amplifies the failure rather than mitigating it. A controlled ablation identifies the mechanism: removing entity-specific clinical evidence from retrieved documents eliminates entity-attribution failure entirely, shifting all failures to confabulation. The two failure modes respond to the same trigger, taking different paths. Production measurement across 740 drug-disease pairs finds 7.8% overall DG in a deployed RAG system, rising to 13.6% for recently approved drugs. Entity-attribution verification (checking that cited evidence applies to the queried entity) detects DG at 97.0% precision and 98.7% DG recall (IPW-adjusted human gold standard); no existing framework implements it.

Problem

• deceptive grounding
• entity attribution
• retrieval-augmented generation
• clinical evidence
• hallucination

Innovation

• deceptive grounding
• entity attribution failure
• retrieval-augmented generation
• clinical AI evaluation
• factuality verification