Text Style Transfer with Large Language Models: Enhancing Medical Anamnesis Transcriptions

Abstract

This study investigates the use of Large Language Models (LLMs) for enhancing medical anamnesis transcriptions via Text Style Transfer (TST). It involves benchmarking three models (Phi3, Llama, and Mistral) and fine-tuning Mistral based on evaluator feedback. Mistral achieved the best initial performance but showed limited improvement after fine-tuning. The work highlights the challenges of adapting LLMs to clinical tasks and discusses limitations such as data quality and evaluator bias. Future directions include extended training, dataset expansion, and exploring new machine learning techniques.