提升阿拉伯语医疗大模型性能，发现语言混合训练更关键
Bridging Language Barriers in Healthcare: A Study on Arabic LLMs
- 通过调整训练数据的语言比例，优化阿拉伯语医疗模型
- 大模型配合精准语言配比，在本地临床任务上表现更优
- 适合需要多语言医疗AI的开发者和研究者
本文研究了在多语言理解与医学知识双重要求下构建大型语言模型（LLMs）的挑战。实验表明，仅翻译医疗数据无法保证目标语言临床任务的高性能。不同医学任务的最佳训练语言组合差异显著。我们发现，经过精心校准语言比例的大模型在本族语临床任务中表现更优。此外，结果提示，仅依赖微调难以有效融入新语言知识，仍需数据和计算密集型的预训练方法才能在多语言医疗场景中实现最佳性能。这些发现为构建面向多元语言群体的高效、包容性医疗AI系统提供了重要指导。
原文摘要 · Abstract (English)
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.
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