# CURE: A Dataset for Clinical Understanding & Retrieval Evaluation

arXiv: https://arxiv.org/abs/2412.06954 · HTML: https://mlsanity.com/paper/2412.06954/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Dec 2024.

## Built on by (most cited first, 5 of 5)

- [MPIB: A Benchmark for Medical Prompt Injection Attacks and Clinical Safety in LLMs](https://mlsanity.com/paper/2602.06268/) (2026)
- [Lighting the Way for BRIGHT: Reproducible Baselines with Anserini, Pyserini, and RankLLM](https://mlsanity.com/paper/2509.02558/) (2025)
- [Modular Expert Merging for Biomedical Retrieval](https://mlsanity.com/paper/2602.04731/) (2026)
- [HAKARI-Bench: A Lightweight Benchmark for Comparing Retrieval Architectures and Efficiency Settings under Unified Conditions](https://mlsanity.com/paper/2606.22778/) (2026)
- [MMed-Bench-IR: A Heterogeneous Benchmark for Multilingual Medical Information Retrieval](https://mlsanity.com/paper/2606.24200/) (2026)

## Abstract (arXiv)

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.