Overview of the ClinIQLink 2025 Shared Task on Medical Question-Answering
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| Format: | Preprint |
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2025
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| _version_ | 1866913914722189312 |
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| author | Colelough, Brandon Bartels, Davis Demner-Fushman, Dina |
| author_facet | Colelough, Brandon Bartels, Davis Demner-Fushman, Dina |
| contents | In this paper, we present an overview of ClinIQLink, a shared task, collocated with the 24th BioNLP workshop at ACL 2025, designed to stress-test large language models (LLMs) on medically-oriented question answering aimed at the level of a General Practitioner. The challenge supplies 4,978 expert-verified, medical source-grounded question-answer pairs that cover seven formats: true/false, multiple choice, unordered list, short answer, short-inverse, multi-hop, and multi-hop-inverse. Participating systems, bundled in Docker or Apptainer images, are executed on the CodaBench platform or the University of Maryland's Zaratan cluster. An automated harness (Task 1) scores closed-ended items by exact match and open-ended items with a three-tier embedding metric. A subsequent physician panel (Task 2) audits the top model responses. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2506_21597 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Overview of the ClinIQLink 2025 Shared Task on Medical Question-Answering Colelough, Brandon Bartels, Davis Demner-Fushman, Dina Computation and Language Artificial Intelligence Information Retrieval I.2.7 In this paper, we present an overview of ClinIQLink, a shared task, collocated with the 24th BioNLP workshop at ACL 2025, designed to stress-test large language models (LLMs) on medically-oriented question answering aimed at the level of a General Practitioner. The challenge supplies 4,978 expert-verified, medical source-grounded question-answer pairs that cover seven formats: true/false, multiple choice, unordered list, short answer, short-inverse, multi-hop, and multi-hop-inverse. Participating systems, bundled in Docker or Apptainer images, are executed on the CodaBench platform or the University of Maryland's Zaratan cluster. An automated harness (Task 1) scores closed-ended items by exact match and open-ended items with a three-tier embedding metric. A subsequent physician panel (Task 2) audits the top model responses. |
| title | Overview of the ClinIQLink 2025 Shared Task on Medical Question-Answering |
| topic | Computation and Language Artificial Intelligence Information Retrieval I.2.7 |
| url | https://arxiv.org/abs/2506.21597 |