5 papers
ADEPT: Continual Pretraining via Adaptive Expansion and Dynamic Decoupled Tuning
Jinyang Zhang, Yue Fang, Hongxin Ding +5
Conventional continual pretraining (CPT) for large language model (LLM) domain adaptation often suffers from catastrophic forgetting and limited domain capacity. Existing strategie…
Toward Better EHR Reasoning in LLMs: Reinforcement Learning with Expert Attention Guidance
Yue Fang, Yuxin Guo, Jiaran Gao +9
Improving large language models (LLMs) for electronic health record (EHR) reasoning is essential for enabling accurate and generalizable clinical predictions. While LLMs excel at m…
DFAMS: Dynamic-flow guided Federated Alignment based Multi-prototype Search
Zhibang Yang, Xinke Jiang, Rihong Qiu +8
Federated Retrieval (FR) routes queries across multiple external knowledge sources, to mitigate hallucinations of LLMs, when necessary external knowledge is distributed. However, e…
CLAS: A Machine Learning Enhanced Framework for Exploring Large 3D Design Datasets
XiuYu Zhang, Xiaolei Ye, Jui-Che Chang +1
Three-dimensional (3D) objects have wide applications. Despite the growing interest in 3D modeling in academia and industries, designing and/or creating 3D objects from scratch rem…
3DS: Medical Domain Adaptation of LLMs via Decomposed Difficulty-based Data Selection
Hongxin Ding, Yue Fang, Runchuan Zhu +6
Large Language Models(LLMs) excel in general tasks but struggle in specialized domains like healthcare due to limited domain-specific knowledge.Supervised Fine-Tuning(SFT) data con…