MIRA-Ev:A Benchmark for Granular Evidence Detection and Relational Reasoning in Clinical Exams

Abstract:Clinical NLP evaluation remains dominated by multiple-choice question answering (MCQA), which scores only final-answer accuracy and cannot detect when a model reaches the correct diagnosis while grounding it in irrelevant, absent, or contradictory evidence. We introduce MIRA-Ev, a clinical argument mining benchmark built on Spanish Médico Interno Residente (MIR) licensing-exam cases, re-annotated by expert clinicians with span-level premises, claims, and directed support/attack relations, and released in parallel Spanish (native), English, and Basque versions, the first clinical argumentation resource in Basque. MIRA-Ev organizes evaluation into a three-tier task hierarchy: evidence sentence retrieval, argumentative component extraction, and relation classification.

Computation and Language (cs.CL); Artificial Intelligence (cs.AI)

arXiv:2607.19201 [cs.CL]

(or arXiv:2607.19201v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2607.19201 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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