1 citations · 2 across the 8 of their papers we have counts for
8 papers
Membership Inference for Contrastive Pre-training Models with Text-only PII Queries
Ruoxi Cheng, Yizhong Ding, Jian Zhao +5
Contrastive pretraining models such as CLIP and CLAP, serve as the ubiquitous perceptual backbones for modern multimodal large models, yet their reliance on web-scale data raises g…
DMRL: Data- and Model-aware Reward Learning for Data Extraction
Zhiqiang Wang, Ruoxi Cheng
Large language models (LLMs) are inherently vulnerable to unintended privacy breaches. Consequently, systematic red-teaming research is essential for developing robust defense mech…
Inverse Reinforcement Learning with Dynamic Reward Scaling for LLM Alignment
Ruoxi Cheng, Haoxuan Ma, Weixin Wang +7
Alignment is vital for safely deploying large language models (LLMs). Existing techniques are either reward-based (training a reward model on preference pairs and optimizing with r…
PBI-Attack: Prior-Guided Bimodal Interactive Black-Box Jailbreak Attack for Toxicity Maximization
Ruoxi Cheng, Yizhong Ding, Shuirong Cao +6
Understanding the vulnerabilities of Large Vision Language Models (LVLMs) to jailbreak attacks is essential for their responsible real-world deployment. Most previous work requires…
Gibberish is All You Need for Membership Inference Detection in Contrastive Language-Audio Pretraining
Ruoxi Cheng, Yizhong Ding, Shuirong Cao +2
Audio can disclose PII, particularly when combined with related text data. Therefore, it is essential to develop tools to detect privacy leakage in Contrastive Language-Audio Pretr…
AGR: Age Group fairness Reward for Bias Mitigation in LLMs
Shuirong Cao, Ruoxi Cheng, Zhiqiang Wang
LLMs can exhibit age biases, resulting in unequal treatment of individuals across age groups. While much research has addressed racial and gender biases, age bias remains little ex…
TUNI: A Textual Unimodal Detector for Identity Inference in CLIP Models
Songze Li, Ruoxi Cheng, Xiaojun Jia
The widespread usage of large-scale multimodal models like CLIP has heightened concerns about the leakage of PII. Existing methods for identity inference in CLIP models require que…
Reinforcement Learning from Multi-role Debates as Feedback for Bias Mitigation in LLMs
Ruoxi Cheng, Haoxuan Ma, Shuirong Cao +6
Bias in LLMs can harm user experience and societal outcomes. However, current bias mitigation methods often require intensive human feedback, lack transferability to other topics o…