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    <abstract>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.</abstract>
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%0 Conference Proceedings
%T Overview of the ClinIQLink 2025 Shared Task on Medical Question-Answering
%A Colelough, Brandon
%A Bartels, Davis
%A Demner-Fushman, Dina
%Y Demner-Fushman, Dina
%Y Ananiadou, Sophia
%Y Miwa, Makoto
%Y Tsujii, Junichi
%S Proceedings of the 24th Workshop on Biomedical Language Processing
%D 2025
%8 August
%I Association for Computational Linguistics
%C Viena, Austria
%@ 979-8-89176-275-6
%F colelough-etal-2025-overview
%X 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.
%R 10.18653/v1/2025.bionlp-1.32
%U https://aclanthology.org/2025.bionlp-1.32/
%U https://doi.org/10.18653/v1/2025.bionlp-1.32
%P 378-387

Markdown (Informal)

[Overview of the ClinIQLink 2025 Shared Task on Medical Question-Answering](https://aclanthology.org/2025.bionlp-1.32/) (Colelough et al., BioNLP 2025)

• Overview of the ClinIQLink 2025 Shared Task on Medical Question-Answering (Colelough et al., BioNLP 2025)

ACL

• Brandon Colelough, Davis Bartels, and Dina Demner-Fushman. 2025. Overview of the ClinIQLink 2025 Shared Task on Medical Question-Answering. In Proceedings of the 24th Workshop on Biomedical Language Processing, pages 378–387, Viena, Austria. Association for Computational Linguistics.