MIRA-Ev:A Benchmark for Granular Evidence Detection and Relational Reasoning in Clinical Exams
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
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- FuzzySimilar title/name (fuzzy) · 59%jeinlee1991/chinese-llm-benchmark →
“Fuzzy title match (0.73): “MIRA-Ev:A Benchmark for Granular Evidence Detection and Rela” ≈ “jeinlee1991/chinese-llm-benchmark””
- LinkedLinked via arxiv author · 85%Iker De la Iglesia →
“MIRA-Ev:A Benchmark for Granular Evidence Detection and Relational Reasoning in Clinical Exams”
- LinkedLinked via arxiv author · 85%Johanna Ramirez-Romero →
“MIRA-Ev:A Benchmark for Granular Evidence Detection and Relational Reasoning in Clinical Exams”
- LinkedLinked via arxiv author · 85%Jose Maria Villa-Gonzalez →
“MIRA-Ev:A Benchmark for Granular Evidence Detection and Relational Reasoning in Clinical Exams”
- LinkedLinked via arxiv author · 85%Irune Urroz García →
“MIRA-Ev:A Benchmark for Granular Evidence Detection and Relational Reasoning in Clinical Exams”
- LinkedLinked via arxiv author · 85%Ander Barrena →
“MIRA-Ev:A Benchmark for Granular Evidence Detection and Relational Reasoning in Clinical Exams”
- LinkedLinked via arxiv author · 85%Aitziber Atutxa →
“MIRA-Ev:A Benchmark for Granular Evidence Detection and Relational Reasoning in Clinical Exams”
- PossiblePossibly related (embedding) · 46%Open-weight 4B models approach o3-level medical question answering in Swedish [P] →