Bridging Language Barriers in Healthcare: A Study on Arabic LLMs
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arXiv
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866917894699352064 |
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| author | Saadi, Nada Raha, Tathagata Christophe, Clément Pimentel, Marco AF Rajan, Ronnie Kanithi, Praveen K |
| author_facet | Saadi, Nada Raha, Tathagata Christophe, Clément Pimentel, Marco AF Rajan, Ronnie Kanithi, Praveen K |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_09825 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Bridging Language Barriers in Healthcare: A Study on Arabic LLMs Saadi, Nada Raha, Tathagata Christophe, Clément Pimentel, Marco AF Rajan, Ronnie Kanithi, Praveen K Computation and Language Artificial Intelligence 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. |
| title | Bridging Language Barriers in Healthcare: A Study on Arabic LLMs |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2501.09825 |