弥合医疗领域的语言壁垒：阿拉伯语大语言模型研究
Bridging Language Barriers in Healthcare: A Study on Arabic LLMs
AI总结：
该研究针对多语言医学大语言模型开发难题，通过实验发现训练数据最优语言组合因任务而异，更大模型经语言比例校准后母语临床表现更优，指出预训练比微调更能提升多语言医疗性能，为普惠医疗AI构建提供指导。
摘要：
本文探究了开发同时精通多语言理解与医学知识的大语言模型（LLM）所面临的挑战。我们证明，仅翻译医学数据并不能保证模型在目标语言的临床任务上取得优异表现。实验显示，训练数据中的最优语言组合在不同医学任务间存在显著差异。我们发现，经过语言比例精心校准的更大规模模型，在母语临床任务上能实现更优性能。此外，研究结果表明，仅依赖微调可能并非为LLM注入新语言知识的最有效方式；相反，数据密集型且计算密集型的预训练方法，对于在多语言医疗场景中实现最优性能或许仍必不可少。这些发现为面向不同语言群体构建有效、普惠的医疗AI系统提供了宝贵指导。
英文摘要：
This paper investigates the challenges of developing large language models (LLMs) proficient in both multilingual understanding and medical knowledge. We demonstrate that simply translating medical data does not guarantee strong performance on clinical tasks in the target language. Our experiments reveal that the optimal language mix in training data varies significantly across different medical tasks. We find that larger models with carefully calibrated language ratios achieve superior performance on native-language clinical tasks. Furthermore, our results suggest that relying solely on fine-tuning may not be the most effective approach for incorporating new language knowledge into LLMs. Instead, data and computationally intensive pretraining methods may still be necessary to achieve optimal performance in multilingual medical settings. These findings provide valuable guidance for building effective and inclusive medical AI systems for diverse linguistic communities.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
