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.

翻译：鉴于密集检索器在超出其训练数据分布时泛化能力不足，领域特定的测试集对于评估检索性能至关重要。目前面向临床诊疗场景下医疗工作者使用的检索系统测试数据集较为匮乏。为填补这一空白，我们与医学专家合作构建了CURE——一个包含2000个查询的段落排序特设检索测试数据集，涵盖10个医学领域，包含单语（英语）及两种跨语言（法语/西班牙语→英语）检索条件。本文详细阐述了CURE的构建方法，并通过基线实验结果验证其作为评估工具的有效性。CURE采用知识共享署名非商业性4.0许可协议发布，可通过Hugging Face平台获取。