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.
