MedInjection-FR Enhances Biomedical AI with Diverse Data

The research introduces MedInjection-FR, a large-scale dataset consisting of 571,000 instruction-response pairs specifically designed for biomedical instruction tuning in French. This dataset, sourced from native, synthetic, and translated data, aims to overcome the limitations posed by the scarcity of quality instruction data in the French medical domain. Utilizing the Qwen-4B-Instruct model across various configurations, performance evaluations indicate that native data performed best, while mixed data setups can yield beneficial results. The implications of this study extend beyond the dataset itself; it signifies a strategic move towards enhancing the capability of AI in the medical field, particularly in French-speaking regions. By leveraging diverse data sources, this research demonstrates a commitment to improving AI performance while fostering data authenticity and adaptation strategies that can address local language gaps. This development potentially increases national AI autonomy by reducing dependency on foreign data sources for training models in specialized fields.