7 papers
Mitigating the Safety-utility Trade-off in LLM Alignment via Adaptive Safe Context Learning
Yanbo Wang, Minzheng Wang, Jian Liang +3
While reasoning models have achieved remarkable success in complex reasoning tasks, their increasing power necessitates stringent safety measures. For safety alignment, the core ch…
A Comprehensive Survey on Trustworthiness in Reasoning with Large Language Models
Yanbo Wang, Yongcan Yu, Jian Liang +1
The development of Long-CoT reasoning has advanced LLM performance across various tasks, including language understanding, complex problem solving, and code generation. This paradi…
Test-Time Immunization: A Universal Defense Framework Against Jailbreaks for (Multimodal) Large Language Models
Yongcan Yu, Yanbo Wang, Ran He +1
While (multimodal) large language models (LLMs) have attracted widespread attention due to their exceptional capabilities, they remain vulnerable to jailbreak attacks. Various defe…
AUTO: Adaptive Outlier Optimization for Test-Time OOD Detection
Puning Yang, Jian Liang, Jie Cao +1
Out-of-distribution (OOD) detection aims to detect test samples that do not fall into any training in-distribution (ID) classes. Prior efforts focus on regularizing models with ID…
Sample Correlation for Fingerprinting Deep Face Recognition
Jiyang Guan, Jian Liang, Yanbo Wang +1
Face recognition has witnessed remarkable advancements in recent years, thanks to the development of deep learning techniques.However, an off-the-shelf face recognition model as a…
Exploring Vacant Classes in Label-Skewed Federated Learning
Kuangpu Guo, Yuhe Ding, Jian Liang +3
Label skews, characterized by disparities in local label distribution across clients, pose a significant challenge in federated learning. As minority classes suffer from worse accu…