4 papers
Safe-FedLLM: Delving into the Safety of Federated Large Language Models
Mingxiang Tao, Yu Tian, Wenxuan Tu +3
Federated learning (FL) addresses privacy and data-silo issues in the training of large language models (LLMs). Most prior work focuses on improving the efficiency of federated lea…
Red Teaming Large Reasoning Models
Jiawei Chen, Yang Yang, Chao Yu +6
Large Reasoning Models (LRMs) have emerged as a powerful advancement in multi-step reasoning tasks, offering enhanced transparency and logical consistency through explicit chains o…
Adaptive Debiasing Tsallis Entropy for Test-Time Adaptation
Xiangyu Wu, Dongming Jiang, Feng Yu +5
Mainstream Test-Time Adaptation (TTA) methods for adapting vision-language models, e.g., CLIP, typically rely on Shannon Entropy (SE) at test time to measure prediction uncertainty…
Who Can See Through You? Adversarial Shielding Against VLM-Based Attribute Inference Attacks
Yucheng Fan, Jiawei Chen, Yu Tian +1
As vision-language models (VLMs) become widely adopted, VLM-based attribute inference attacks have emerged as a serious privacy concern, enabling adversaries to infer private attri…