Fine-tuning a local LLaMA-3 large language model for automated privacy-preserving physician letter generation in radiation oncology.

ABSTRACT:

Introduction

Generating physician letters is a time-consuming task in daily clinical practice.

Methods

This study investigates local fine-tuning of large language models (LLMs), specifically LLaMA models, for physician letter generation in a privacy-preserving manner within the field of radiation oncology.

Results

Our findings demonstrate that base LLaMA models, without fine-tuning, are inadequate for effectively generating physician letters. The QLoRA algorithm provides an efficient method for local intra-institutional fine-tuning of LLMs with limited computational resources (i.e., a single 48 GB GPU workstation within the hospital). The fine-tuned LLM successfully learns radiation oncology-specific information and generates physician letters in an institution-specif

SUBMITTER: Hou Y 

PROVIDER: S-EPMC11772293 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature