Bridging Language Barriers in Healthcare: A Study on Arabic LLMs
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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Nada Saadi
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2025-01-20T14:12:06.609Z
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Nada Saadi
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Nadas31
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Tathagata Raha
admin_assigned
2025-01-20T14:12:13.246Z
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Tathagata Raha
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tathagataraha
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Clément Christophe
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2025-01-20T14:12:19.413Z
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Clément Christophe
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cchristophe
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Marco AF Pimentel
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Ronnie Rajan
admin_assigned
2025-01-20T14:12:33.526Z
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Ronnie Rajan
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ronnierajan
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Praveen K Kanithi
claimed_verified
2025-01-20T14:07:22.890Z
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Praveenkumar
user
pkanithi
Bridging Language Barriers in Healthcare: A Study on Arabic LLMs
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.
Training large language models with carefully calibrated language ratios in multilingual medical data improves performance on clinical tasks more effectively than fine-tuning alone.
large language models multilingual understanding medical knowledge clinical tasks language mix language ratios pretraining methods fine-tuning medical AI systems