Stochastic Meta-Unlearning: Bridging Language Backbone and Multimodal Unlearning
A new preprint introduces Stochastic Meta-Unlearning (SMU), a bilevel framework designed to improve machine unlearning in vision-language models (VLMs). SMU leverages VLM-level feedback to optimize the language backbone for unlearning, addressing the challenge that text-only unlearning is insufficient when image information is present. Experimental results on two VLMs and multiple datasets show that SMU achieves superior forget-retain trade-offs compared to existing baselines and demonstrates transferability to new unlearning targets and methods.
Why it matters: This work advances the field of machine unlearning by demonstrating that multimodal feedback is essential for effective and transferable unlearning in vision-language models.
Full story at: arXiv Computation and Language ↗