5 papers
TopoCurate:Modeling Interaction Topology for Tool-Use Agent Training
Jinluan Yang, Yuxin Liu, Zhengyu Chen +7
Training tool-use agents typically relies on outcome-based filtering: Supervised Fine-Tuning (SFT) on successful trajectories and Reinforcement Learning (RL) on pass-rate-selected…
Mix Data or Merge Models? Balancing the Helpfulness, Honesty, and Harmlessness of Large Language Model via Model Merging
Jinluan Yang, Dingnan Jin, Anke Tang +10
Achieving balanced alignment of large language models (LLMs) in terms of Helpfulness, Honesty, and Harmlessness (3H optimization) constitutes a cornerstone of responsible AI. Exist…
Unifying Adversarial Perturbation for Graph Neural Networks
Jinluan Yang, Ruihao Zhang, Zhengyu Chen +2
This paper studies the vulnerability of Graph Neural Networks (GNNs) to adversarial attacks on node features and graph structure. Various methods have implemented adversarial train…
Discovering Invariant Neighborhood Patterns for Heterophilic Graphs
Jinluan Yang, Ruihao Zhang, Zhengyu Chen +4
This paper studies the problem of distribution shifts on non-homophilous graphs Mosting existing graph neural network methods rely on the homophilous assumption that nodes from the…
Leveraging Invariant Principle for Heterophilic Graph Structure Distribution Shifts
Jinluan Yang, Zhengyu Chen, Teng Xiao +3
Heterophilic Graph Neural Networks (HGNNs) have shown promising results for semi-supervised learning tasks on graphs. Notably, most real-world heterophilic graphs are composed of a…