8 citations · 21 across the 17 of their papers we have counts for
7 papers · 1 filter
HalluTracer: Pre-Decoding Truthfulness Prediction via Depth-Averaged Probe-Logit
Zhihao Guo, Zonghan Wu, Huan Huo +6
Internal-state probes enable truthfulness prediction before a large language model generates an answer. When detectors change both the layers they read and the rules used to combin…
Dual Inversion for Text-to-Image Diffusion Models: From Both Prompt and Noise Perspectives
Xiaolong Liu, Junjian Li, Yuan Xiao +4
Prompt inversion, as a typical reverse engineering technique, enables text-to-image (T2I) diffusion models to generate the desired target images without extensive prompt engineerin…
One Framework for All: Cross-Modal Membership Inference for Generative Models
Dayong Ye, Tainqing Zhu, Kun Gao +6
Large generative models across text-to-text, text-to-image, and image-to-text modalities have been shown to pose significant privacy risks. One fundamental threat is membership inf…
Auditing Machine Unlearning: A Systematic Research on Whether Models Truly Forget
Dayong Ye, Tianqing Zhu, Ruiding Huang +5
Machine unlearning has been extensively studied in response to growing privacy concerns and regulatory requirements. However, auditing whether unlearning algorithms have truly eras…
Secure Forgetting: A Framework for Privacy-Driven Unlearning in Large Language Model (LLM)-Based Agents
Dayong Ye, Tainqing Zhu, Congcong Zhu +5
Large language model (LLM)-based agents have recently gained considerable attention due to the powerful reasoning capabilities of LLMs. Existing research predominantly focuses on e…
From Spark to Fire: Modeling and Mitigating Error Cascades in LLM-Based Multi-Agent Collaboration
Yizhe Xie, Congcong Zhu, Xinyue Zhang +5
Large Language Model-based Multi-Agent Systems (LLM-MAS) are increasingly applied to complex collaborative scenarios. However, their collaborative mechanisms may cause minor inaccu…
Forgetting Similar Samples: Can Machine Unlearning Do it Better?
Heng Xu, Tianqing Zhu, Dayong Ye +3
Machine unlearning, a process enabling pre-trained models to remove the influence of specific training samples, has attracted significant attention in recent years. Although extens…