MIRA-Ev:A Benchmark for Granular Evidence Detection and Relational Reasoning in Clinical Exams
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Computer Science > Computation and Language
Title: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.
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2607.19201 [cs.CL] |
| (or arXiv:2607.19201v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2607.19201
arXiv-issued DOI via DataCite (pending registration)
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