Fine-tuning large language models for improved health communication in low-resource languages

Background: The reported study illustrates a methodology for compiling training datasets to fine-tune Large Language Models (LLMs) for healthcare information in Vietnamese, a low-resource language. The objective is to bridge the gap in medical information accessibility and enhance healthcare communication in developing countries by adapting LLMs to specific linguistic nuances and domain needs. Method: The methodology involves selecting a base model, compiling a domain-specific dataset, and fine-tuning the model with this dataset. Three open-source models were selected. The dataset, comprising approximately 337,000 prompt-response pairs in Vietnamese, was compiled using existing datasets, data crawled from Vietnamese medical online forums, and distilled from Vietnamese medical textbooks. The three models were finetuned using the Low-Rank adaptation (LoRA) and Quantized Low-Rank adaptation (QLoRA) techniques. Models' performances were evaluated using BertScore score, Rouge-L score, and the "LLM-as-a-Judge" method. Results: The fine-tuned models showed enhancements in performance over their base versions across evaluation metrics in BertScore score, Rouge-L score and "LLM-as-a-Judge" method, confirming the effectiveness of the finetuning process. This study details the process of fine-tuning open-source LLMs for health information inquiries in Vietnamese, demonstrating its potential to improve healthcare communication in low-resource languages. Deploying the fine-tuned LLM on-premise enhances data privacy and security. However, the significant computing power and costs required pose challenges, especially for organizations in developing countries. Conclusion: This case study highlights the unique challenges faced by developing countries using low-resource languages. Initiatives are needed to emphasize efforts to bridge healthcare gaps in underserved areas and contribute to global health equity.