BioMistral: A Collection of Open-Source Pretrained Large Language Models
  for Medical Domains
Yanis Labrak Adrien Bazoge Emmanuel Morin Pierre-Antoine Gourraud Mickael Rouvier Richard Dufour
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.