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arXiv · 2607.19201

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

Iker De la Iglesia·Johanna Ramirez-Romero·Jose Maria Villa-Gonzalez·Irune Urroz García·Ander Barrena·Aitziber Atutxa
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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 M\'edico 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.

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Iker De la Iglesia, Johanna Ramirez-Romero, Jose Maria Villa-Gonzalez, Irune Urroz García, Ander Barrena, Aitziber Atutxa. 2026-07-21. MIRA-Ev:A Benchmark for Granular Evidence Detection and Relational Reasoning in Clinical Exams. https://arxiv.org/abs/2607.19201

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