1 citations · 1 across the 15 of their papers we have counts for
4 papers · 1 filter
COME: Test-time adaption by Conservatively Minimizing Entropy
Qingyang Zhang, Yatao Bian, Xinke Kong +2
Machine learning models must continuously self-adjust themselves for novel data distribution in the open world. As the predominant principle, entropy minimization (EM) has been pro…
Probing the Safety Response Boundary of Large Language Models via Unsafe Decoding Path Generation
Haoyu Wang, Bingzhe Wu, Yatao Bian +3
Large Language Models (LLMs) are implicit troublemakers. While they provide valuable insights and assist in problem-solving, they can also potentially serve as a resource for malic…
One Fits All: Learning Fair Graph Neural Networks for Various Sensitive Attributes
Yuchang Zhu, Jintang Li, Yatao Bian +2
Recent studies have highlighted fairness issues in Graph Neural Networks (GNNs), where they produce discriminatory predictions against specific protected groups categorized by sens…
Step-On-Feet Tuning: Scaling Self-Alignment of LLMs via Bootstrapping
Haoyu Wang, Guozheng Ma, Ziqiao Meng +9
Self-alignment is an effective way to reduce the cost of human annotation while ensuring promising model capability. However, most current methods complete the data collection and…