4 papers
Stackelberg Self-Annotation: A Robust Approach to Data-Efficient LLM Alignment
Xu Chu, Zhixin Zhang, Tianyu Jia +1
Aligning large language models (LLMs) with human preferences typically demands vast amounts of meticulously curated data, which is both expensive and prone to labeling noise. We pr…
IntelliCare: Improving Healthcare Analysis with Variance-Controlled Patient-Level Knowledge from Large Language Models
Zhihao Yu, Yujie Jin, Yongxin Xu +3
While pioneering deep learning methods have made great strides in analyzing electronic health record (EHR) data, they often struggle to fully capture the semantics of diverse medic…
SMART: Towards Pre-trained Missing-Aware Model for Patient Health Status Prediction
Zhihao Yu, Xu Chu, Yujie Jin +2
Electronic health record (EHR) data has emerged as a valuable resource for analyzing patient health status. However, the prevalence of missing data in EHR poses significant challen…
LoRA Dropout as a Sparsity Regularizer for Overfitting Control
Yang Lin, Xinyu Ma, Xu Chu +4
Parameter-efficient fine-tuning methods, represented by LoRA, play an essential role in adapting large-scale pre-trained models to downstream tasks. However, fine-tuning LoRA-serie…