6 papers
Efficient Text-Attributed Graph Learning through Selective Annotation and Graph Alignment
Huanyi Xie, Lijie Hu, Lu Yu +6
In the realm of Text-attributed Graphs (TAGs), traditional graph neural networks (GNNs) often fall short due to the complex textual information associated with each node. Recent me…
Backdooring CLIP through Concept Confusion
Lijie Hu, Junchi Liao, Weimin Lyu +5
Backdoor attacks pose a serious threat to deep learning models by allowing adversaries to implant hidden behaviors that remain dormant on clean inputs but are maliciously triggered…
Private Language Models via Truncated Laplacian Mechanism
Tianhao Huang, Tao Yang, Ivan Habernal +2
Deep learning models for NLP tasks are prone to variants of privacy attacks. To prevent privacy leakage, researchers have investigated word-level perturbations, relying on the form…
Faithful Interpretation for Graph Neural Networks
Lijie Hu, Tianhao Huang, Lu Yu +3
Currently, attention mechanisms have garnered increasing attention in Graph Neural Networks (GNNs), such as Graph Attention Networks (GATs) and Graph Transformers (GTs). It is not…
Semi-supervised Concept Bottleneck Models
Lijie Hu, Tianhao Huang, Huanyi Xie +6
Concept Bottleneck Models (CBMs) have garnered increasing attention due to their ability to provide concept-based explanations for black-box deep learning models while achieving hi…
Prompt-SAW: Leveraging Relation-Aware Graphs for Textual Prompt Compression
Muhammad Asif Ali, Zhengping Li, Shu Yang +8
Large Language Models (LLMs) have shown exceptional abilities for multiple different natural language processing tasks. While prompting is a crucial tool for LLM inference, we obse…