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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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“As a result, open-source models such as BioMistral may offer a viable solution6.”
From Current applications and challenges in large language models for patient care: a systematic review (2025) · cites this paper as [6]
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- Samuel Ainsworth, Jonathan Hayase, and Siddhartha Srinivasa. 2023. Git re-basin: Merging models mod- ulo permutation symmetries. In The Eleventh Inter- national Conference on Learning Representations.
- Devansh Arpit, Huan Wang, Yingbo Zhou, and Caim- ing Xiong. 2022. Ensemble of averages: Improving model selection and boosting performance in domain generalization. In Advances in Neural Information Processing Systems.
- Zerui Cai, Taolin Zhang, Chengyu Wang, and Xiaofeng He. 2021. Embert: A pre-trained language model for chinese medical text mining. In Web and Big Data, pages 242-257, Cham. Springer International Publishing.
- Junbum Cha, Sanghyuk Chun, Kyungjae Lee, Han- Cheol Cho, Seunghyun Park, Yunsung Lee, and Sun- grae Park. 2021. Swad: Domain generalization by seeking flat minima. In Advances in Neural Informa- tion Processing Systems, volume 34, pages 22405- 22418.
- Tim Dettmers, Mike Lewis, Younes Belkada, and Luke Zettlemoyer. 2022. GPT3.int8(): 8-bit matrix mul- tiplication for transformers at scale. In Advances in Neural Information Processing Systems.
- Tim Dettmers, Artidoro Pagnoni, Ari Holtzman, and Luke Zettlemoyer. 2023. QLoRA: Efficient finetun- ing of quantized LLMs. In Thirty-seventh Confer- ence on Neural Information Processing Systems.
- Jwala Dhamala, Tony Sun, Varun Kumar, Satyapriya Krishna, Yada Pruksachatkun, Kai-Wei Chang, and Rahul Gupta. 2021. Bold: Dataset and metrics for measuring biases in open-ended language genera- tion. In Proceedings of the 2021 ACM conference on fairness, accountability, and transparency, pages 862-872.
- Jason Fries, Leon Weber, Natasha Seelam, Gabriel Al- tay, Debajyoti Datta, Samuele Garda, Sunny Kang, Rosaline Su, Wojciech Kusa, Samuel Cahyawijaya, Fabio Barth, Simon Ott, Matthias Samwald, Stephen Bach, Stella Biderman, Mario Sänger, Bo Wang, Alison Callahan, Daniel León Periñán, Théo Gi- gant, Patrick Haller, Jenny Chim, Jose Posada, John Giorgi, Karthik Rangasai Sivaraman, Marc Pàmies, Marianna Nezhurina, Robert Martin, Michael Cul- lan, Moritz Freidank, Nathan Dahlberg, Shubhan- shu Mishra, Shamik Bose, Nicholas Broad, Yanis Labrak, Shlok Deshmukh, Sid Kiblawi, Ayush Singh, Minh Chien Vu, Trishala Neeraj, Jonas Golde, Albert Villanova del Moral, and Benjamin Beilharz. 2022. Bigbio: A framework for data-centric biomedical natural language processing. In Advances in Neural Information Processing Systems, volume 35, pages 25792-25806.
- Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, and Jacob Steinhardt. 2021. Measuring massive multitask language under- standing. In International Conference on Learning Representations.
- Gabriel Ilharco, Marco Tulio Ribeiro, Mitchell Worts- man, Ludwig Schmidt, Hannaneh Hajishirzi, and Ali Farhadi. 2023. Editing models with task arithmetic. In The Eleventh International Conference on Learn- ing Representations.
- Gabriel Ilharco, Mitchell Wortsman, Samir Yitzhak Gadre, Shuran Song, Hannaneh Hajishirzi, Simon Kornblith, Ali Farhadi, and Ludwig Schmidt. 2022. Patching open-vocabulary models by interpolating weights. In Advances in Neural Information Process- ing Systems.
- Harold Jeffreys. 1946. An invariant form for the prior probability in estimation problems. Proceedings of the Royal Society of London. Series A. Mathematical and Physical Sciences, 186(1007):453-461.
- Xisen Jin, Xiang Ren, Daniel Preotiuc-Pietro, and Pengxiang Cheng. 2023. Dataless knowledge fu- sion by merging weights of language models. In The Eleventh International Conference on Learning Representations.
- Percy Liang, Rishi Bommasani, Tony Lee, Dimitris Tsipras, Dilara Soylu, Michihiro Yasunaga, Yian Zhang, Deepak Narayanan, Yuhuai Wu, Ananya Ku- mar, Benjamin Newman, Binhang Yuan, Bobby Yan, Ce Zhang, Christian Alexander Cosgrove, Christo- pher D Manning, Christopher Re, Diana Acosta- Navas, Drew Arad Hudson, Eric Zelikman, Esin Durmus, Faisal Ladhak, Frieda Rong, Hongyu Ren, Huaxiu Yao, Jue WANG, Keshav Santhanam, Laurel Orr, Lucia Zheng, Mert Yuksekgonul, Mirac Suzgun, Nathan Kim, Neel Guha, Niladri S. Chatterji, Omar Khattab, Peter Henderson, Qian Huang, Ryan An- drew Chi, Sang Michael Xie, Shibani Santurkar, Surya Ganguli, Tatsunori Hashimoto, Thomas Icard, Tianyi Zhang, Vishrav Chaudhary, William Wang, Xuechen Li, Yifan Mai, Yuhui Zhang, and Yuta Ko- reeda. 2023. Holistic evaluation of language models. Transactions on Machine Learning Research. Fea- tured Certification, Expert Certification.
- Ji Lin, Jiaming Tang, Haotian Tang, Shang Yang, Xingyu Dang, and Song Han. 2023. Awq: Activation- aware weight quantization for llm compression and acceleration. arXiv preprint arXiv:2306.00978. · arXiv
- Ankit Pal, Logesh Kumar Umapathi, and Malaikannan Sankarasubbu. 2022. Medmcqa: A large-scale multi- subject multi-choice dataset for medical domain ques- tion answering. In Proceedings of the Conference on Health, Inference, and Learning, volume 174 of Proceedings of Machine Learning Research, pages 248-260.
- PMLR.
- John Platt et al. 1999. Probabilistic outputs for support vector machines and comparisons to regularized like- lihood methods. Advances in large margin classifiers, 10(3):61-74.
- Rafael Rafailov, Archit Sharma, Eric Mitchell, Christo- pher D Manning, Stefano Ermon, and Chelsea Finn. 2023. Direct preference optimization: Your language model is secretly a reward model. In Thirty-seventh Conference on Neural Information Processing Sys- tems.
- Colin Raffel, Noam Shazeer, Adam Roberts, Kather- ine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J. Liu. 2020. Exploring the limits of transfer learning with a unified text-to-text transformer. Journal of Machine Learning Research, 21(140):1-67.
- Sidak Pal Singh and Martin Jaggi. 2020. Model fu- sion via optimal transport. In Advances in Neural Information Processing Systems, volume 33, pages 22045-22055.
- Rian Touchent, Laurent Romary, and Eric De La Clerg- erie. 2023. CamemBERT-bio : Un modèle de langue français savoureux et meilleur pour la santé. In Actes de CORIA-TALN 2023. Actes de la 30e Con- férence sur le Traitement Automatique des Langues Naturelles (TALN), volume 1 : travaux de recherche originaux -articles longs, pages 323-334, Paris, France. ATALA.
- BigScience Workshop, :, Teven Le Scao, Angela Fan, Christopher Akiki, Ellie Pavlick, Suzana Ilić, Daniel Hesslow, Roman Castagné, Alexandra Sasha Luc- cioni, François Yvon, Matthias Gallé, Jonathan Tow, Alexander M. Rush, Stella Biderman, Albert Web- son, Pawan Sasanka Ammanamanchi, Thomas Wang, Benoît Sagot, Niklas Muennighoff, Albert Villanova del Moral, Olatunji Ruwase, Rachel Bawden, Stas Bekman, Angelina McMillan-Major, Iz Beltagy, Huu Nguyen, Lucile Saulnier, Samson Tan, Pedro Or- tiz Suarez, Victor Sanh, Hugo Laurençon, Yacine Jernite, and et al. Julien Launay. 2023. Bloom: A 176b-parameter open-access multilingual language model.
- Mitchell Wortsman, Gabriel Ilharco, Samir Ya Gadre, Rebecca Roelofs, Raphael Gontijo-Lopes, Ari S Mor- cos, Hongseok Namkoong, Ali Farhadi, Yair Car- mon, Simon Kornblith, and Ludwig Schmidt. 2022. Model soups: averaging weights of multiple fine- tuned models improves accuracy without increasing inference time. In Proceedings of the 39th Interna- tional Conference on Machine Learning, volume 162 of Proceedings of Machine Learning Research, pages 23965-23998.
- PMLR.
- Prateek Yadav, Derek Tam, Leshem Choshen, Colin Raffel, and Mohit Bansal. 2023. TIES-merging: Re- solving interference when merging models. In Thirty- seventh Conference on Neural Information Process- ing Systems.
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Chen, Z., Chen, S., Ning, Y., Zhang, Q., Wang, B., Yu, B., Li, Y., Liao, Z., Wei, C., Lu, Z., et al. (2024) · arXiv · doi:10.48550/arxiv.2410.05080 · Cited by 7
Bazoge, A., Wargny, M., Constant dit Beaufils, P., Morin, E., Daille, B., Gourraud, P.-A., & Hadjadj, S. (2025) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2025.110609 · Cited by 6
Self-citationFarías, H., Aroca, J. G., & Ortiz, D. (2025) · Technologies · doi:10.3390/technologies13040140 · Cited by 6
Rullo, R., Maatouk, A., Huang, T., Chen, J., Qiu, W., O’Connor, G., Womack, J. A., Sadak, T. I., Rodriguez, C., Carneiro, P. B., et al. (2025) · Journal of Medical Internet Research · doi:10.2196/77053 · Cited by 8
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Rouhizadeh, H., Yazdani, A., Zhang, B., Alvarez, D. V., Hüser, M., Vanobberghen, A., Yang, R., Li, I., Walter, A., & Teodoro, D. (2025) · medRxiv · doi:10.1101/2025.01.15.25320579 · Cited by 6
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Blašković, L., Tanković, N., Lorencin, I., & Šegota, S. B. (2025) · Big Data and Cognitive Computing · doi:10.3390/bdcc9100256 · Cited by 4
Evangelista, E. D. L., Ruba, F., Bukhari, S., Nazir, A., & Sharma, R. (2025) · JMIR Diabetes · doi:10.2196/76454 · Cited by 5
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Zhu, Y., Gao, J., Wang, Z., Liao, W., Zheng, X., Liang, L., Bernabéu, M. O., Wang, Y., Yu, L., Pan, C., et al. (2026) · npj Digital Medicine · doi:10.1038/s41746-026-02539-z · Cited by 17
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Idrissi-Yaghir, A., Arzideh, K., Schäfer, H., Eryılmaz, B., Bahn, M., Wen, Y., Borys, K., Hartmann, E. M., Schmidt, C. S., Pelka, O., et al. (2025) · Journal of Medical Internet Research · doi:10.2196/73540 · Cited by 7
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Doneva, S. E., Qin, S., Sick, B., Ellendorff, T., Goldman, J.-P., Schneider, G., & Ineichen, B. V. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.04.19.588095 · Cited by 5
Choi, I., Long, Q., & Getzen, E. J. (2024) · medRxiv · doi:10.1101/2024.05.06.24306959 · Cited by 5
Yang, S., Luo, Y., Fotedar, N., Jiao, M., Rao, V. R., Ju, X., Wu, S., Xian, X., Sun, H., Karakis, I., et al. (2024) · medRxiv · doi:10.1101/2024.05.26.24307955 · Cited by 5
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Huang, S., Lu, W. P., Xu, Z., Cheng, Q., Yang, J., & Huang, Y. (2025) · Information Processing & Management · doi:10.1016/j.ipm.2025.104207 · Cited by 6
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Pinto, J. G. D. S., de Freitas, A. R., Martins, A. R. D. C., Sawazaki, C. M. R., Vidal, C., & Oliveira, L. E. S. E. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-79038-6_4 · Cited by 5
Guimarães, A., Magalhães, J., & Martins, B. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-88717-8_5 · Cited by 4
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Neeley, M. B., Qi, G., Wang, G., Tang, R., Mao, D., Liu, C., Pasupuleti, S., Yuan, B., Xia, F., Liu, P., et al. (2024) · Bioinformatics Advances · doi:10.1093/bioadv/vbaf148 · Cited by 9
Rüb, M., Herbst, J., Mey, O., Irmer, R., Lipps, C., & Schotten, H. D. (2024) · IEEE International Conference on Cybernetics, Cognition and Machine Learning Applications (ICCCMLA) · doi:10.1109/icccmla63077.2024.10871706 · Cited by 4
Rashed, E. A., Hussain, W., Mousa, M., & Al-Shatouri, M. A. (2025) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi60581.2025.10981080 · Cited by 5
Metadata
- 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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