Apollo: A Lightweight Multilingual Medical LLM towards Democratizing Medical AI to 6B People
<|reference_start|>Apollo: A Lightweight Multilingual Medical LLM towards Democratizing Medical AI to 6B People: Despite the vast repository of global medical knowledge predominantly being in English, local languages are crucial for delivering tailored healthcare services, particularly in areas with limited medical resources. To extend the reach of medical AI advancements to a broader population, we aim to develop medical LLMs across the six most widely spoken languages, encompassing a global population of 6.1 billion. This effort culminates in the creation of the ApolloCorpora multilingual medical dataset and the XMedBench benchmark. In the multilingual medical benchmark, the released Apollo models, at various relatively-small sizes (i.e., 0.5B, 1.8B, 2B, 6B, and 7B), achieve the best performance among models of equivalent size. Especially, Apollo-7B is the state-of-the-art multilingual medical LLMs up to 70B. Additionally, these lite models could be used to improve the multi-lingual medical capabilities of larger models without fine-tuning in a proxy-tuning fashion. We will open-source training corpora, code, model weights and evaluation benchmark.<|reference_end|>
@article{wang2024apollo:,
    title={Apollo: A Lightweight Multilingual Medical LLM towards Democratizing
  Medical AI to 6B People},
    author={Xidong Wang, Nuo Chen, Junyin Chen, Yidong Wang, Guorui Zhen, Chunxian
  Zhang, Xiangbo Wu, Yan Hu, Anningzhe Gao, Xiang Wan, Haizhou Li, Benyou Wang},
    journal={arXiv preprint arXiv:2403.03640},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.03640},
    primaryClass={cs.CL cs.AI}
}