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

Natural language processing is a collection of techniques designed to empower computer systems to comprehend and/or produce human language. The purpose of this investigation was to train several large language models (LLMs) to explore the tradeoff between model complexity and performance while classifying narrative feedback on trainees into the Accreditation Council for Graduate Medical Education subcompetencies. We hypothesized that classification accuracy would increase with model complexity.