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

2026-07-10 · Source: Computation and Language · Field: Technology & Digital — Artificial Intelligence & Machine Learning, AI in Healthcare · Depth: Expert, quick

Summary

Clinical Retrieval-Augmented Generation (RAG) systems can exhibit "deceptive grounding" (DG), a failure where model claims are factually sourced from retrieved documents but attributed to the wrong entity. This issue is invisible to standard faithfulness, hallucination, and citation checks. A controlled factorial benchmark across 13 models revealed DG rates from 8% to 87% under adversarial conditions, with medical and biomedical fine-tuned models reaching up to 86.7%, indicating domain specialization amplifies this failure. Removing entity-specific clinical evidence from retrieved documents eliminated DG, shifting failures to confabulation. Production measurement across 740 drug-disease pairs found 7.8% overall DG in a deployed RAG system, rising to 13.6% for recently approved drugs. Entity-attribution verification, a novel check, detects DG with 97.0% precision and 98.7% recall.

Key takeaway

For AI Scientists and Machine Learning Engineers evaluating clinical RAG systems, you must implement entity-attribution verification. Standard hallucination and faithfulness checks are insufficient, as deceptive grounding can misattribute correct information, especially for specialized models and new drugs. Your evaluation pipeline should include explicit checks to ensure cited evidence directly pertains to the queried entity, mitigating a significant, hidden failure mode that affects up to 13.6% of responses for recently approved drugs.

Key insights

Deceptive grounding in clinical RAG systems presents factually correct but misattributed information, bypassing standard evaluation metrics.

Principles

• Domain specialization can amplify RAG attribution failures.
• Standard RAG evaluation metrics are insufficient for entity attribution.
• Entity-specific evidence removal shifts failure from DG to confabulation.

Method

Entity-attribution verification involves checking if cited evidence applies to the queried entity.

In practice

• DG rates can reach 87% in adversarial RAG conditions.
• Recently approved drugs show higher DG (13.6%) in production.
• Entity-attribution verification achieves 97.0% precision, 98.7% recall.

Topics

• Clinical Retrieval-Augmented Generation
• Deceptive Grounding
• Entity Attribution
• RAG Evaluation
• Medical AI
• Hallucination Detection

Best for: AI Architect, AI Engineer, NLP Engineer, AI Scientist, Research Scientist, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Computation and Language.