Given the dominance of dense retrievers that do not generalize well beyond their training dataset distributions, domain-specific test sets are essential in evaluating retrieval. There are few test datasets for retrieval systems intended for use by healthcare providers in a point-of-care setting. To fill this gap we have collaborated with medical professionals to create CURE, an ad-hoc retrieval test dataset for passage ranking with 2000 queries spanning 10 medical domains with a monolingual (English) and two cross-lingual (French/Spanish -> English) conditions. In this paper, we describe how CURE was constructed and provide baseline results to showcase its effectiveness as an evaluation tool. CURE is published with a Creative Commons Attribution Non Commercial 4.0 license and can be accessed on Hugging Face and as a retrieval task on MTEB.
@article{arxiv.2412.06954,
title = {CURE: A Dataset for Clinical Understanding & Retrieval Evaluation},
author = {Nadia Athar Sheikh and Daniel Buades Marcos and Anne-Laure Jousse and Akintunde Oladipo and Olivier Rousseau and Jimmy Lin},
journal= {arXiv preprint arXiv:2412.06954},
year = {2025}
}