Towards Conditioning Clinical Text Generation for User Control
Journal:
arXiv
Published Date:
Feb 24, 2025
Abstract
Deploying natural language generation systems in clinical settings remains
challenging despite advances in Large Language Models (LLMs), which continue to
exhibit hallucinations and factual inconsistencies, necessitating human
oversight. This paper explores automated dataset augmentation using LLMs as
human proxies to condition LLMs for clinician control without increasing
cognitive workload. On the BioNLP ACL'24 Discharge Me! Shared Task, we achieve
new state-of-the-art results with simpler methods than prior submissions
through more efficient training, yielding a 9\% relative improvement without
augmented training and up to 34\% with dataset augmentation. Preliminary human
evaluation further supports the effectiveness of our approach, highlighting the
potential of augmenting clinical text generation for control to enhance
relevance, accuracy, and factual consistency.
