6 papers
EvolvR: Self-Evolving Pairwise Reasoning for Story Evaluation to Enhance Generation
Xinda Wang, Zhengxu Hou, Yangshijie Zhang +6
Although the effectiveness of Large Language Models (LLMs) as judges (LLM-as-a-judge) has been validated, their performance remains limited in open-ended tasks, particularly in sto…
Style Attack Disguise: When Fonts Become a Camouflage for Adversarial Intent
Yangshijie Zhang, Xinda Wang, Jialin Liu +3
With social media growth, users employ stylistic fonts and font-like emoji to express individuality, creating visually appealing text that remains human-readable. However, these fo…
Multi-task Adversarial Attacks against Black-box Model with Few-shot Queries
Wenqiang Wang, Yan Xiao, Hao Lin +2
Current multi-task adversarial text attacks rely on abundant access to shared internal features and numerous queries, often limited to a single task type. As a result, these attack…
No Query, No Access
Wenqiang Wang, Siyuan Liang, Yangshijie Zhang +3
Textual adversarial attacks mislead NLP models, including Large Language Models (LLMs), by subtly modifying text. While effective, existing attacks often require knowledge of the v…
Incomplete In-context Learning
Wenqiang Wang, Yangshijie Zhang
Large vision language models (LVLMs) achieve remarkable performance through Vision In-context Learning (VICL), a process that depends significantly on demonstrations retrieved from…
Emoti-Attack: Zero-Perturbation Adversarial Attacks on NLP Systems via Emoji Sequences
Yangshijie Zhang
Deep neural networks (DNNs) have achieved remarkable success in the field of natural language processing (NLP), leading to widely recognized applications such as ChatGPT. However,…