Fine-Tuning Large Language Models to Enhance Programmatic Assessment in Graduate Medical Education

Abstract

Transformer-based LLMs were fine-tuned to understand anesthesiology graduate medical education language. Complex LLMs did not outperform FastText. However, equivalent performance was achieved with a model that was 94% smaller, which may allow model deployment on personal devices to enhance speed and data privacy. This work advances our understanding of best practices when integrating LLMs into graduate medical education.

Ähnliche Arbeiten