6 papers · 1 filter
Hyperball May Not Be a Free Lunch
Yihao Xiao, Jialong Sun, Zitian Gao +5
For scale-invariant deep networks, Hyperball-style optimizers have shown strong performance in large-scale training by fixing the norms of matrix-valued parameters and normalizing…
RAPO: Risk-Aware Preference Optimization for Generalizable Safe Reasoning
Zeming Wei, Qiaosheng Zhang, Xia Hu +1
Large Reasoning Models (LRMs) have achieved tremendous success with their chain-of-thought (CoT) reasoning, yet also face safety issues similar to those of basic language models. I…
SMI: Statistical Membership Inference for Reliable Unlearned Model Auditing
Jialong Sun, Zeming Wei, Jiaxuan Zou +6
Machine unlearning (MU) is essential for enforcing the right to be forgotten in machine learning systems. A key challenge of MU is how to reliably audit whether a model has truly f…
Reliable Unlearning Harmful Information in LLMs with Metamorphosis Representation Projection
Chengcan Wu, Zeming Wei, Huanran Chen +2
While Large Language Models (LLMs) have demonstrated impressive performance in various domains and tasks, concerns about their safety are becoming increasingly severe. In particula…
Unveiling the Basin-Like Loss Landscape in Large Language Models
Huanran Chen, Yinpeng Dong, Zeming Wei +4
We discover the emergence of \textit{basins} in the loss landscape of large language models. As model scale increases, LLMs become progressively more resilient to random perturbati…
Towards the Worst-case Robustness of Large Language Models
Huanran Chen, Yinpeng Dong, Zeming Wei +2
Recent studies have revealed the vulnerability of large language models to adversarial attacks, where adversaries craft specific input sequences to induce harmful, violent, private…