6 papers
Training-Free Token-Level Steering for LLM Personalized Co-Writing
Wenhao Mao, Chengbin Hou, Weixiao Wang +4
While Large Language Models (LLMs) show great promise for personalization, they often lack specialized domain knowledge. Conventional solutions like fine-tuning struggle with high…
FedAGHN: Personalized Federated Learning with Attentive Graph HyperNetworks
Jiarui Song, Yunheng Shen, Chengbin Hou +4
Personalized Federated Learning (PFL) aims to address the statistical heterogeneity of data across clients by learning the personalized model for each client. Among various PFL app…
Enhancing Noise Robustness of Parkinson's Disease Telemonitoring via Contrastive Feature Augmentation
Ziming Tang, Chengbin Hou, Tianyu Zhang +3
Parkinson's disease (PD) is one of the most common neurodegenerative disorder. PD telemonitoring emerges as a novel assessment modality enabling self-administered at-home tests of…
Parse Trees Guided LLM Prompt Compression
Wenhao Mao, Chengbin Hou, Tianyu Zhang +3
Offering rich contexts to Large Language Models (LLMs) has shown to boost the performance in various tasks, but the resulting longer prompt would increase the computational cost an…
Look Inside for More: Internal Spatial Modality Perception for 3D Anomaly Detection
Hanzhe Liang, Guoyang Xie, Chengbin Hou +3
3D anomaly detection has recently become a significant focus in computer vision. Several advanced methods have achieved satisfying anomaly detection performance. However, they typi…
Node Importance Estimation Leveraging LLMs for Semantic Augmentation in Knowledge Graphs
Xinyu Lin, Tianyu Zhang, Chengbin Hou +3
Node Importance Estimation (NIE) is a task that quantifies the importance of node in a graph. Recent research has investigated to exploit various information from Knowledge Graphs…