1 citations · 1 across the 8 of their papers we have counts for
8 papers
CASA: Classification Augmented with Safety Attention for Robust Multimodal Alignment
Anurag Kumar, Raghuveer Peri, Jon Burnsky +4
Multimodal large-language models (MLLMs) often experience degraded safety alignment when harmful queries exploit cross-modal interactions. Models aligned on text alone show a highe…
A Closer Look at Adversarial Suffix Learning for Jailbreaking LLMs: Augmented Adversarial Trigger Learning
Zhe Wang, Yanjun Qi
Gradient optimization-based adversarial attack methods automate the learning of adversarial triggers to generate jailbreak prompts or leak system prompts. In this work, we take a c…
TurboFuzzLLM: Turbocharging Mutation-based Fuzzing for Effectively Jailbreaking Large Language Models in Practice
Aman Goel, Xian Carrie Wu, Zhe Wang +2
Jailbreaking large-language models (LLMs) involves testing their robustness against adversarial prompts and evaluating their ability to withstand prompt attacks that could elicit u…
Graph of Attacks with Pruning: Optimizing Stealthy Jailbreak Prompt Generation for Enhanced LLM Content Moderation
Daniel Schwartz, Dmitriy Bespalov, Zhe Wang +2
As large language models (LLMs) become increasingly prevalent, ensuring their robustness against adversarial misuse is crucial. This paper introduces the GAP (Graph of Attacks with…
Hierarchical Prompt Decision Transformer: Improving Few-Shot Policy Generalization with Global and Adaptive Guidance
Zhe Wang, Haozhu Wang, Yanjun Qi
Decision transformers recast reinforcement learning as a conditional sequence generation problem, offering a simple but effective alternative to traditional value or policy-based m…
TaeBench: Improving Quality of Toxic Adversarial Examples
Xuan Zhu, Dmitriy Bespalov, Liwen You +2
Toxicity text detectors can be vulnerable to adversarial examples - small perturbations to input text that fool the systems into wrong detection. Existing attack algorithms are tim…
Less is More for Improving Automatic Evaluation of Factual Consistency
Tong Wang, Ninad Kulkarni, Yanjun Qi
Assessing the factual consistency of automatically generated texts in relation to source context is crucial for developing reliable natural language generation applications. Recent…
LaRS: Latent Reasoning Skills for Chain-of-Thought Reasoning
Zifan Xu, Haozhu Wang, Dmitriy Bespalov +3
Chain-of-thought (CoT) prompting is a popular in-context learning (ICL) approach for large language models (LLMs), especially when tackling complex reasoning tasks. Traditional ICL…