The paper introduces CoLabScience with PULI, a positive-unlabeled RL framework for proactive interventions in streaming biomedical dialogues, plus the BSDD benchmark dataset, claiming superior performance over baselines.
Title resolution pending
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
cs.CL 2verdicts
UNVERDICTED 2representative citing papers
Fine-tuning and data augmentation improve LLM performance on medical jargon extraction and prioritization from EHR notes, with augmented open-source models sometimes outperforming closed-source ones on 106 annotated notes.
citing papers explorer
-
"Excuse me, may I say something..." CoLabScience, A Proactive AI Assistant for Biomedical Discovery and LLM-Expert Collaborations
The paper introduces CoLabScience with PULI, a positive-unlabeled RL framework for proactive interventions in streaming biomedical dialogues, plus the BSDD benchmark dataset, claiming superior performance over baselines.
-
Enhancing LLMs for Identifying and Prioritizing Important Medical Jargons from Electronic Health Record Notes Utilizing Data Augmentation
Fine-tuning and data augmentation improve LLM performance on medical jargon extraction and prioritization from EHR notes, with augmented open-source models sometimes outperforming closed-source ones on 106 annotated notes.