1 citations · 3 across the 19 of their papers we have counts for
5 papers · 1 filter
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…
The Illusion of Progress? A Critical Look at Test-Time Adaptation for Vision-Language Models
Lijun Sheng, Jian Liang, Ran He +2
Test-time adaptation (TTA) methods have gained significant attention for enhancing the performance of vision-language models (VLMs) such as CLIP during inference, without requiring…
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…
Do We Really Need Curated Malicious Data for Safety Alignment in Multi-modal Large Language Models?
Yanbo Wang, Jiyang Guan, Jian Liang +1
Multi-modal large language models (MLLMs) have made significant progress, yet their safety alignment remains limited. Typically, current open-source MLLMs rely on the alignment inh…
R-TPT: Improving Adversarial Robustness of Vision-Language Models through Test-Time Prompt Tuning
Lijun Sheng, Jian Liang, Zilei Wang +1
Vision-language models (VLMs), such as CLIP, have gained significant popularity as foundation models, with numerous fine-tuning methods developed to enhance performance on downstre…