J. Ahn

REFINE: Closing the Loop Between Large Language Models and Symbolic Rules in Clinical NLP

This study investigates whether large language models (LLMs) can assist in identifying extraction errors and generating candidate rules to improve symbolic clinical NLP systems and suggests that LLMs can support scalable rule refinement for symbolic clinical NLP systems.