title: Bridging Language Barriers in Healthcare: A Study on Arabic LLMs
summary: 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.
paper_authors._id: 678e3e0c8aeb001443af5cb1
paper_authors.name: Nada Saadi
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-01-20T14:12:06.609Z
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paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/66bb35988b09ede0b7b92313/M06mQ3ifyRwuladTNwMS2.png
paper_authors.fullname: Nada Saadi
paper_authors.type: user
paper_authors.user: Nadas31
paper_authors._id: 678e3e0c8aeb001443af5cb2
paper_authors.name: Tathagata Raha
paper_authors.status: admin_assigned
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paper_authors.avatarUrl: /avatars/06b2eb3a5d27864280d4d02e6d00d782.svg
paper_authors.fullname: Tathagata Raha
paper_authors.type: user
paper_authors.user: tathagataraha
paper_authors._id: 678e3e0c8aeb001443af5cb3
paper_authors.name: Clément Christophe
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-01-20T14:12:19.413Z
paper_authors._id: 628e39f4b1596566033b8d7b
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/628e39f4b1596566033b8d7b/-Y807up1cgMmAQsczdOPn.jpeg
paper_authors.fullname: Clément Christophe
paper_authors.type: user
paper_authors.user: cchristophe
paper_authors._id: 678e3e0c8aeb001443af5cb4
paper_authors.name: Marco AF Pimentel
paper_authors._id: 678e3e0c8aeb001443af5cb5
paper_authors.name: Ronnie Rajan
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-01-20T14:12:33.526Z
paper_authors._id: 65281d6ef61ca80b9c2ee707
paper_authors.avatarUrl: /avatars/090ea7210a4bb6549b0f7fee71525625.svg
paper_authors.fullname: Ronnie Rajan
paper_authors.type: user
paper_authors.user: ronnierajan
paper_authors._id: 678e3e0c8aeb001443af5cb6
paper_authors.name: Praveen K Kanithi
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-01-20T14:07:22.890Z
paper_authors._id: 65280984b794fe3d06544d77
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/65280984b794fe3d06544d77/tyrxbxtDG02On1uiRaVbL.jpeg
paper_authors.fullname: Praveenkumar
paper_authors.type: user
paper_authors.user: pkanithi
paper_title: Bridging Language Barriers in Healthcare: A Study on Arabic LLMs
paper_summary: 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.
paper_ai_keywords: large language models (LLMs) multilingual understanding medical knowledge clinical tasks language mix training data language ratios fine-tuning pretraining methods multilingual medical settings medical AI systems