1 citations · 2 across the 11 of their papers we have counts for
7 papers · 1 filter
Dissecting Representation Misalignment in Contrastive Learning via Influence Function
Lijie Hu, Chenyang Ren, Huanyi Xie +5
Contrastive learning, commonly applied in large-scale multimodal models, often relies on data from diverse and often unreliable sources, which can include misaligned or mislabeled…
Dissecting Fine-Tuning Unlearning in Large Language Models
Yihuai Hong, Yuelin Zou, Lijie Hu +3
Fine-tuning-based unlearning methods prevail for preventing targeted harmful, sensitive, or copyrighted information within large language models while preserving overall capabiliti…
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…
Exploring the Personality Traits of LLMs through Latent Features Steering
Shu Yang, Shenzhe Zhu, Liang Liu +3
Large language models (LLMs) have significantly advanced dialogue systems and role-playing agents through their ability to generate human-like text. While prior studies have shown…
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…
Editable Concept Bottleneck Models
Lijie Hu, Chenyang Ren, Zhengyu Hu +5
Concept Bottleneck Models (CBMs) have garnered much attention for their ability to elucidate the prediction process through a humanunderstandable concept layer. However, most previ…