Towards building multilingual language model for medicine
Open accessAbstract
The development of open-source, multilingual medical language models can benefit a wide, linguistically diverse audience from different regions. To promote this domain, we present contributions from the following: First, we construct a multilingual medical corpus, containing approximately 25.5B tokens encompassing 6 main languages, termed as MMedC, enabling auto-regressive domain adaptation for general LLMs; Second, to monitor the development of multilingual medical LLMs, we propose a multilingual medical multi-choice question-answering benchmark with rationale, termed as MMedBench; Third, we have assessed a number of open-source large language models (LLMs) on our benchmark, along with those further auto-regressive trained on MMedC. Our final model, MMed-Llama 3, with only 8B parameters, achieves superior performance compared to all other open-source models on both MMedBench and English benchmarks, even rivaling GPT-4. In conclusion, in this work, We present a large-scale corpus, a benchmark and a series of models to support the development of multilingual medical LLMs.
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All results were obtained using a 3-shot prompting strategy except for MCQA tasks, where a zero-shot prompting setting was used to align with previous studies.
“All results presented here were obtained using a 3-shot prompting strategy (more details in the “Evaluation Settings” section in Supplementary), except for MCQA tasks, where we used a zero-shot prompting setting to align with previous studies7,8,19.”
From Towards evaluating and building versatile large language models for medicine (2025) · cites this paper as [19]
MMedIns-Llama 3's superior multilingual rationale performance is likely due to its base language model (MMed-Llama 3) being initially developed to be multilingual.
“This superior performance is likely due to the fact that our base language model (MMed-Llama 3) was initially developed to be multilingual19.”
From Towards evaluating and building versatile large language models for medicine (2025) · cites this paper as [19]
Further auto-regressive training on medical-related corpus can inject medical knowledge into models, allowing them to perform better in different downstream tasks.
“We take the same approach as our previous work7,19, which have shown that further auto-regressive training on medical-elated corpus can inject medical knowledge into the models, thus allowing them to perform better in different downstream tasks.”
From Towards evaluating and building versatile large language models for medicine (2025) · cites this paper as [19]
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Towards evaluating and building versatile large language models for medicine
Wu, C., Qiu, P., Liu, J., Gu, H., Li, N., Zhang, Y., Wang, Y., & Xie, W. (2025) · npj Digital Medicine · doi:10.1038/s41746-024-01390-4 · Cited by 52 · see citing works
Self-citationQuantifying the reasoning abilities of LLMs on clinical cases
Qiu, P., Wu, C., Liu, S., Fan, Y., Zhao, W., Chen, Z., Gu, H., Peng, C., Zhang, Y., Wang, Y., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-64769-1 · Cited by 53 · see citing works
Self-citationWang, Y., Wang, Y., Liang, J., Xu, Y., Liu, Y., Xu, Y., Jiang, Z., Li, Z., Lin, C., Li, F., et al. (2025) · Intelligent Data Analysis: An International Journal · doi:10.1177/1088467x251389769
Self-citation
- Authors
- Qiu, P., Wu, C., Zhang, X., Lin, W., Wang, H., Zhang, Y., Wang, Y., & Xie, W.
- Affiliations
- Shanghai Jiao Tong University (CN)
- Corresponding author
- Wang, Yanfeng
- Xie, Weidi
- ORCID
- Venue
- Nature Communications
- Published
- 2024-09-27
- Volume
- 15
- Issue
- 1
- Pages
- 8384-8384
- ISSN
- 2041-1723 (ISSN-L)
- PMC
- PMC11436924
- OpenAlex
- W4402923149
- Cited by
- 147
- References
- 49
- Citations by year
- 2026: 67 · 2025: 70 · 2024: 10
- FWCI
- 21.65
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Nature Communications (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by-nc-sa)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion, cc-by)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- arXiv (Cornell University) (cc-by)
- Funders
- Science and Technology Commission of Shanghai Municipality (21DZ1100100, 22511106101, BP0719010, 18DZ2270700)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
- Keywords
- open-source large language models, low-resource languages, medical question answering
- MeSH terms
- Humans, Language, Natural Language Processing, Multilingualism, Benchmarking
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