MLLMEraser: Achieving Test-Time Unlearning in Multimodal Large Language Models through Activation Steering

Understand

Multimodal large language models (MLLMs) have demonstrated remarkable capabilities across vision-language tasks, yet their large-scale deployment raises pressing concerns about memorized private data, outdated knowledge, and harmful content.

• Existing unlearning approaches for MLLMs typically adapt training-based strategies such as gradient ascent or preference optimization, but these methods are computationally expensive, irreversible, and often distort retained knowledge.
• In this work, we propose MLLMEraser, an input-aware, training-free framework for test-time unlearning.
• Our approach leverages activation steering to enable dynamic knowledge erasure without parameter updates.