6 citations · 19 across the 9 of their papers we have counts for
10 papers
Bridging Auxiliary Constraints to Resolve Instruction Following in Large Reasoning Models
Zhengyi Zhao, Shubo Zhang, Huimin Wang +7
Large Reasoning Models (LRMs) have demonstrated impressive capabilities in many tasks, yet they struggle with reliably following multiple instructions, either by failing to satisfy…
Think or Not? Exploring Thinking Efficiency in Large Reasoning Models via an Information-Theoretic Lens
Xixian Yong, Xiao Zhou, Yingying Zhang +3
The recent rise of Large Reasoning Models (LRMs) has significantly improved multi-step reasoning performance, but often at the cost of generating excessively long reasoning chains.…
Editing Factual Knowledge and Explanatory Ability of Medical Large Language Models
Derong Xu, Ziheng Zhang, Zhihong Zhu +9
Model editing aims to precisely alter the behaviors of large language models (LLMs) in relation to specific knowledge, while leaving unrelated knowledge intact. This approach has p…
Biomedical Entity Linking as Multiple Choice Question Answering
Zhenxi Lin, Ziheng Zhang, Xian Wu +1
Although biomedical entity linking (BioEL) has made significant progress with pre-trained language models, challenges still exist for fine-grained and long-tailed entities. To addr…
Large Language Model Distilling Medication Recommendation Model
Qidong Liu, Xian Wu, Xiangyu Zhao +4
The recommendation of medication is a vital aspect of intelligent healthcare systems, as it involves prescribing the most suitable drugs based on a patient's specific health needs.…
Improving Biomedical Entity Linking with Retrieval-enhanced Learning
Zhenxi Lin, Ziheng Zhang, Xian Wu +1
Biomedical entity linking (BioEL) has achieved remarkable progress with the help of pre-trained language models. However, existing BioEL methods usually struggle to handle rare and…
Emerging Drug Interaction Prediction Enabled by Flow-based Graph Neural Network with Biomedical Network
Yongqi Zhang, Quanming Yao, Ling Yue +4
Accurately predicting drug-drug interactions (DDI) for emerging drugs, which offer possibilities for treating and alleviating diseases, with computational methods can improve patie…
Relation-aware Ensemble Learning for Knowledge Graph Embedding
Ling Yue, Yongqi Zhang, Quanming Yao +5
Knowledge graph (KG) embedding is a fundamental task in natural language processing, and various methods have been proposed to explore semantic patterns in distinctive ways. In thi…
When MOE Meets LLMs: Parameter Efficient Fine-tuning for Multi-task Medical Applications
Qidong Liu, Xian Wu, Xiangyu Zhao +4
The recent surge in Large Language Models (LLMs) has garnered significant attention across numerous fields. Fine-tuning is often required to fit general LLMs for a specific domain,…
JoTR: A Joint Transformer and Reinforcement Learning Framework for Dialog Policy Learning
Wai-Chung Kwan, Huimin Wang, Hongru Wang +4
Dialogue policy learning (DPL) is a crucial component of dialogue modelling. Its primary role is to determine the appropriate abstract response, commonly referred to as the "dialog…