MIRA-Ev:A Benchmark for Granular Evidence Detection and Relational Reasoning in Clinical Exams
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Abstract (arXiv)
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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References · 10
- 2024ClinicalBench: Can LLMs Beat Traditional ML Models in Clinical Prediction?cited by 31
- 2024Medical Adaptation of Large Language and Vision-Language Models: Are We Making Progress?cited by 28
- 2022Model and Data Transfer for Cross-Lingual Sequence Labelling in Zero-Resource Settingscited by 21
- 2024Biomedical Large Languages Models Seem not to be Superior to Generalist Models on Unseen Medical Datacited by 21
- 2024CasiMedicos-Arg: A Medical Question Answering Dataset Annotated with Explanatory Argumentative Structurescited by 11
- 2023Explanatory Argument Extraction of Correct Answers in Resident Medical Examscited by 6
- 2024Argument Mining in Data Scarce Settings: Cross-lingual Transfer and Few-shot Techniquescited by 6
- 2023HiTZ@Antidote: Argumentation-driven Explainable Artificial Intelligence for Digital Medicinecited by 5
- 2024Ranking Over Scoring: Towards Reliable and Robust Automated Evaluation of LLM-Generated Medical Explanatory Argumentscited by 2
- 2026To Adapt or not to Adapt, Rethinking the Value of Medical Knowledge-Aware Large Language Modelscited by 1
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