This study focuses on fine-tuning Large Language Models (LLMs) for healthcare information in Vietnamese, a low-resource language, to improve medical information accessibility and healthcare communication in developing countries. The methodology involves selecting a base model (BloomZ-3B, LLaMA2–7B and LLaMA2–13B), compiling a domain-specific dataset of approximately 337,000 prompt-response pairs in Vietnamese from existing datasets, Vietnamese medical online forums, and medical textbooks, and fine-tuning the model using Low-Rank adaptation (LoRA) and Quantized Low-Rank adaptation (QLoRA) techniques. The fine-tuned models showed enhanced performance, demonstrating the potential to improve healthcare communication in low-resource languages and enhance data privacy and security.


📂 Paper: https://www.sciencedirect.com/science/article/pii/S0169260725000720/pdfft?md5=b348ebfecc8d8f8b481e23ec241da2de&pid=1-s2.0-S0169260725000720-main.pdf

@scopeofai
https://t.me/LLM_learning