# Overview of the ClinIQLink 2025 Shared Task on Medical Question-Answering

Source: [Overview of the ClinIQLink 2025 Shared Task on Medical Question-Answering](<https://www.sota2.com/research/paper/68fa79b31e209131cea7ef92>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T05:03:44.730Z

Authors: Brandon Colelough, Davis Bartels, Dina Demner-Fushman

Published: 2025-06-18T19:56:32.000Z

## 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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2506.21597>)

[PDF](<https://arxiv.org/pdf/2506.21597.pdf>)