在医疗保健领域消除语言障碍：关于阿拉伯语LLM的研究。

Bridging Language Barriers in Healthcare: A Study on Arabic LLMs

January 16, 2025

作者: Nada Saadi, Tathagata Raha, Clément Christophe, Marco AF Pimentel, Ronnie Rajan, Praveen K Kanithi

cs.AI

摘要

本文探讨了开发大型语言模型（LLMs）以精通多语言理解和医学知识所面临的挑战。我们证明，简单地将医学数据翻译并不保证在目标语言的临床任务中表现出色。我们的实验揭示了在不同医学任务中，训练数据中最佳语言组合存在显著差异。我们发现，具有精心校准语言比例的更大模型在母语临床任务上表现出色。此外，我们的结果表明，仅依赖微调可能不是将新语言知识纳入LLMs的最有效方法。相反，数据和计算密集型的预训练方法仍然可能是在多语言医学环境中实现最佳性能所必需的。这些发现为构建面向不同语言社区的有效和包容性医学人工智能系统提供了宝贵的指导。

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.