6 papers
Stochastic Meta-Unlearning: Bridging Language Backbone and Multimodal Unlearning
Zijie Liu, Jinhao Duan, Gaowen Liu +2
Machine unlearning for vision-language models (VLMs) remains underexplored. Unlike language models, VLMs combine a language backbone with visual components, which makes unlearning…
Safety Mirage: How Spurious Correlations Undermine VLM Safety Fine-Tuning and Can Be Mitigated by Machine Unlearning
Yiwei Chen, Yuguang Yao, Yihua Zhang +3
Recent vision language models (VLMs) have made remarkable strides in generative modeling with multimodal inputs, particularly text and images. However, their susceptibility to gene…
Invisible Watermarks, Visible Gains: Steering Machine Unlearning with Bi-Level Watermarking Design
Yuhao Sun, Yihua Zhang, Gaowen Liu +2
With the increasing demand for the right to be forgotten, machine unlearning (MU) has emerged as a vital tool for enhancing trust and regulatory compliance by enabling the removal…
SEUF: Is Unlearning One Expert Enough for Mixture-of-Experts LLMs?
Haomin Zhuang, Yihua Zhang, Kehan Guo +4
Recent advancements in LLMs unlearning have shown remarkable success in removing unwanted data-model influences while preserving the model's utility for legitimate knowledge. Despi…
Collision- and Reachability-Aware Multi-Robot Control with Grounded LLM Planners
Jiabao Ji, Yongchao Chen, Yang Zhang +4
Large language models (LLMs) have demonstrated strong performance in various robot control tasks. However, their deployment in real-world applications remains constrained. Even sta…
UnlearnCanvas: Stylized Image Dataset for Enhanced Machine Unlearning Evaluation in Diffusion Models
Yihua Zhang, Chongyu Fan, Yimeng Zhang +8
The technological advancements in diffusion models (DMs) have demonstrated unprecedented capabilities in text-to-image generation and are widely used in diverse applications. Howev…