# MediQAl: A French Medical Question Answering Dataset for Knowledge and Reasoning Evaluation

## TL;DR

MediQAl is a French medical dataset for evaluating language models' ability to recall medical facts and reason through clinical scenarios.

## Contribution

MediQAl introduces a new French medical QA dataset with diverse tasks and cognitive labels for evaluating factual recall and reasoning.

## Key findings

- MediQAl contains 32,603 questions across 41 medical subjects with three types of tasks.
- Evaluation with 14 large language models reveals a significant performance gap between factual recall and reasoning tasks.
- The dataset provides a benchmark for French medical QA, addressing a multilingual resource gap.

## Abstract

This work introduces MediQAl, a French medical question answering dataset designed to evaluate the capabilities of language models in factual medical recall and reasoning over real-world clinical scenarios. MediQAl contains 32,603 questions sourced from French medical examinations across 41 medical subjects. The dataset includes three tasks: (i) Multiple-Choice Question with Unique answer, (ii) Multiple-Choice Question with Multiple answer, and (iii) Open-Ended Question with Short-Answer. Each question is labeled as Understanding or Reasoning, enabling a detailed analysis of models’ cognitive capabilities. We validate the MediQAl dataset through extensive evaluation with 14 large language models, including recent reasoning-augmented models, and observe a significant performance gap between factual recall and reasoning tasks. Our evaluation provides a comprehensive benchmark for assessing language models’ performance on French medical question answering, addressing a crucial gap in multilingual resources for the medical domain.