# FrenchMedMCQA: A French Multiple-Choice Question Answering Dataset for Medical domain

arXiv: https://arxiv.org/abs/2304.04280 · HTML: https://mlsanity.com/paper/2304.04280/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 25 references that are in the map.
- First posted on arXiv 9 Apr 2023.

## Builds on (most cited first, 17 of 17)

- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge](https://mlsanity.com/paper/1803.05457/) (2018)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [HuggingFace's Transformers: State-of-the-art Natural Language Processing](https://mlsanity.com/paper/1910.03771/) (2019)
- [Can a Suit of Armor Conduct Electricity? A New Dataset for Open Book Question Answering](https://mlsanity.com/paper/1809.02789/) (2018)
- [BioBERT: a pre-trained biomedical language representation model for biomedical text mining](https://mlsanity.com/paper/1901.08746/) (2019)
- [Leveraging Passage Retrieval with Generative Models for Open Domain Question Answering](https://mlsanity.com/paper/2007.01282/) (2020)
- [Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing](https://mlsanity.com/paper/2007.15779/) (2020)
- [RACE: Large-scale ReAding Comprehension Dataset From Examinations](https://mlsanity.com/paper/1704.04683/) (2017)
- [MedMCQA : A Large-scale Multi-Subject Multi-Choice Dataset for Medical domain Question Answering](https://mlsanity.com/paper/2203.14371/) (2022)
- [UnifiedQA: Crossing Format Boundaries With a Single QA System](https://mlsanity.com/paper/2005.00700/) (2020)
- [CamemBERT: a Tasty French Language Model](https://mlsanity.com/paper/1911.03894/) (2019)
- [QASC: A Dataset for Question Answering via Sentence Composition](https://mlsanity.com/paper/1910.11473/) (2019)
- [MiniLMv2: Multi-Head Self-Attention Relation Distillation for Compressing Pretrained Transformers](https://mlsanity.com/paper/2012.15828/) (2020)
- [HEAD-QA: A Healthcare Dataset for Complex Reasoning](https://mlsanity.com/paper/1906.04701/) (2019)

## Built on by (most cited first, 25 of 25)

- [Towards Building Multilingual Language Model for Medicine](https://mlsanity.com/paper/2402.13963/) (2024)
- [Towards Evaluating and Building Versatile Large Language Models for Medicine](https://mlsanity.com/paper/2408.12547/) (2024)
- [Apollo: A Lightweight Multilingual Medical LLM towards Democratizing Medical AI to 6B People](https://mlsanity.com/paper/2403.03640/) (2024)
- [Distractor Generation in Multiple-Choice Tasks: A Survey of Methods, Datasets, and Evaluation](https://mlsanity.com/paper/2402.01512/) (2024)
- [KorMedMCQA: Multi-Choice Question Answering Benchmark for Korean Healthcare Professional Licensing Examinations](https://mlsanity.com/paper/2403.01469/) (2024)
- [Efficiently Democratizing Medical LLMs for 50 Languages via a Mixture of Language Family Experts](https://mlsanity.com/paper/2410.10626/) (2024)
- [DrBenchmark: A Large Language Understanding Evaluation Benchmark for French Biomedical Domain](https://mlsanity.com/paper/2402.13432/) (2024)
- [Multi-OphthaLingua: A Multilingual Benchmark for Assessing and Debiasing LLM Ophthalmological QA in LMICs](https://mlsanity.com/paper/2412.14304/) (2024)
- [MOLE: Metadata Extraction and Validation in Scientific Papers Using LLMs](https://mlsanity.com/paper/2505.19800/) (2025)
- [GRAF: Graph Retrieval Augmented by Facts for Romanian Legal Multi-Choice Question Answering](https://mlsanity.com/paper/2412.04119/) (2024)
- [PersianMedQA: Evaluating Large Language Models on a Persian-English Bilingual Medical Question Answering Benchmark](https://mlsanity.com/paper/2506.00250/) (2025)
- [Adaptation of Biomedical and Clinical Pretrained Models to French Long Documents: A Comparative Study](https://mlsanity.com/paper/2402.16689/) (2024)
- [Fact or Guesswork? Evaluating Large Language Models' Medical Knowledge with Structured One-Hop Judgments](https://mlsanity.com/paper/2502.14275/) (2025)
- [Biomed-Enriched: A Biomedical Dataset Enriched with LLMs for Pretraining and Extracting Rare and Hidden Content](https://mlsanity.com/paper/2506.20331/) (2025)
- [Pantagruel: Unified Self-Supervised Encoders for French Text and Speech](https://mlsanity.com/paper/2601.05911/) (2026)
- [CollectiveSFT: Scaling Large Language Models for Chinese Medical Benchmark with Collective Instructions in Healthcare](https://mlsanity.com/paper/2407.19705/) (2024)
- [HEAD-QA v2: Expanding a Healthcare Benchmark for Reasoning](https://mlsanity.com/paper/2511.15355/) (2025)
- [MedInjection-FR: Exploring the Role of Native, Synthetic, and Translated Data in Biomedical Instruction Tuning](https://mlsanity.com/paper/2603.06905/) (2026)
- [Domain-specific or Uncertainty-aware models: Does it really make a difference for biomedical text classification?](https://mlsanity.com/paper/2407.12626/) (2024)
- [From Guidelines to Guarantees: A Graph-Based Evaluation Harness for Domain-Specific Evaluation of LLMs](https://mlsanity.com/paper/2508.20810/) (2025)
- [CareMedEval dataset: Evaluating Critical Appraisal and Reasoning in the Biomedical Field](https://mlsanity.com/paper/2511.03441/) (2025)
- [KALE: Enhancing Knowledge Manipulation in Large Language Models via Knowledge-aware Learning](https://mlsanity.com/paper/2601.07430/) (2026)
- [When Tables Go Crazy: Evaluating Multimodal Models on French Financial Documents](https://mlsanity.com/paper/2602.10384/) (2026)
- [Is Biomedical Specialization Still Worth It? Insights from Domain-Adaptive Language Modelling with a New French Health Corpus](https://mlsanity.com/paper/2604.06903/) (2026)
- [Trade-offs in Medical LLM Adaptation: An Empirical Study in French QA](https://mlsanity.com/paper/2606.19266/) (2026)

## Abstract (arXiv)

This paper introduces FrenchMedMCQA, the first publicly available Multiple-Choice Question Answering (MCQA) dataset in French for medical domain. It is composed of 3,105 questions taken from real exams of the French medical specialization diploma in pharmacy, mixing single and multiple answers. Each instance of the dataset contains an identifier, a question, five possible answers and their manual correction(s). We also propose first baseline models to automatically process this MCQA task in order to report on the current performances and to highlight the difficulty of the task. A detailed analysis of the results showed that it is necessary to have representations adapted to the medical domain or to the MCQA task: in our case, English specialized models yielded better results than generic French ones, even though FrenchMedMCQA is in French. Corpus, models and tools are available online.