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When Rubrics Fail: Hallucinations Reveal Blind Spots in Medical AI Evaluation
Hallucinations can undermine clinician trust in LLMs, making it important that evaluation methods capture clinically relevant errors. Rubric-based evaluation has become the leading approach for assessing LLMs in medicine, but it is unclear whether rubric scores reflect such errors. We first study this in a controlled setting using MedHallu, finding that more specific rubrics better distinguish correct from hallucinated responses. To test this systematically, we develop a taxonomy of medical hallucination types and a clinician-validated error-injection pipeline that creates matched correct and
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-11T11:15:46.000Z
First collected: 2026-09-20T18:22:04.777Z. This is not the publication date.