BioMistral: A Collection of Open-Source Pretrained Large Language Models for Medical Domains
Open accessAbstract
Large Language Models (LLMs) have demonstrated remarkable versatility in recent years, offering potential applications across specialized domains such as healthcare and medicine.Despite the availability of various open-source LLMs tailored for health contexts, adapting general-purpose LLMs to the medical domain presents significant challenges.In this paper, we introduce BioMistral, an open-source LLM tailored for the biomedical domain, utilizing Mistral as its foundation model and further pre-trained on PubMed Central.We conduct a comprehensive evaluation of BioMistral on a benchmark comprising 10 established medical question-answering (QA) tasks in English.We also explore lightweight models obtained through quantization and model merging approaches.Our results demonstrate BioMistral's superior performance compared to existing open-source medical models and its competitive edge against proprietary counterparts.Finally, to address the limited availability of data beyond English and to assess the multilingual generalization of medical LLMs, we automatically translated and evaluated this benchmark into 7 other languages.This marks the first large-scale multilingual evaluation of LLMs in the medical domain.Datasets, multilingual evaluation benchmarks, scripts, and all the models obtained during our experiments are freely released.
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Open-source models such as BioMistral may offer a viable solution.
“As a result, open-source models such as BioMistral may offer a viable solution6.”
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- Authors
- Labrak, Y., Bazoge, A., Morin, E., Gourraud, P.-A., Rouvier, M., & Dufour, R.
- Affiliations
- Université d'Avignon et des Pays de Vaucluse (FR)
- École Centrale de Nantes (FR)
- Inserm (FR)
- Venue
- Findings of the Association for Computational Linguistics ACL 2024
- Published
- 2024
- Pages
- 5848-5864
- OpenAlex
- W4391940656
- Cited by
- 269
- References
- 0
- Citations by year
- 2026: 99 · 2025: 117 · 2024: 53
- FWCI
- 79.26
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Findings of the Association for Computational Linguistics: ACL (Findings of ACL) (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion, public-domain)
- HAL (submittedVersion, cc-by-nd)
- HAL (submittedVersion, public-domain)
- arXiv (Cornell University) (public-domain)
- Funders
- Agence Nationale de la Recherche (ANR-23-IAS1-0005)
- Grand Équipement National De Calcul Intensif
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
- Keywords
- open-source large language models, medical question answering, Mistral, PubMed Central, domain adaptation, quantization, model merging, multilingual evaluation, multilingual translation, biomedical text
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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