4 papers
English is Not All You Need: Systematically Exploring the Role of Multilinguality in LLM Post-Training
Mehak Dhaliwal, Shashwat Chaurasia, Yao Qin +2
Despite the widespread multilingual deployment of large language models, post-training pipelines remain predominantly English-centric, contributing to performance disparities acros…
Flaw or Artifact? Rethinking Prompt Sensitivity in Evaluating LLMs
Andong Hua, Kenan Tang, Chenhe Gu +3
Prompt sensitivity, referring to the phenomenon where paraphrasing (i.e., repeating something written or spoken using different words) leads to significant changes in large languag…
Improving Adversarial Transferability in MLLMs via Dynamic Vision-Language Alignment Attack
Chenhe Gu, Jindong Gu, Andong Hua +1
Multimodal Large Language Models (MLLMs), built upon LLMs, have recently gained attention for their capabilities in image recognition and understanding. However, while MLLMs are vu…
Conflict-Aware Adversarial Training
Zhiyu Xue, Haohan Wang, Yao Qin +1
Adversarial training is the most effective method to obtain adversarial robustness for deep neural networks by directly involving adversarial samples in the training procedure. To…