Memory Forgetting Adapter Sculpting for Selective Multimodal Large Language Model Unlearning

ICLR 2026 Conference Submission49 Authors

Keywords: MLLMs, Machine Unlearning, MLLM Unlearning, Privacy Protection

Abstract: Multimodal Large Language Models (MLLMs) achieve remarkable capabilities but can inadvertently memorize privacy-sensitive information. Existing unlearning methods can remove such knowledge, yet they often degrade the model’s general image understanding. To address this, we propose the Sculpted Memory Forgetting Adapter (SMFA), which confines forgetting to targeted memory regions while preserving overall capabilities. SMFA first fine-tunes the model to replace sensitive responses with refusals, yielding a memory forgetting adapter, and then applies retaining anchor-guided masking mechanism to prevent interference with unrelated knowledge and understanding ability. To systematically evaluate selective unlearning, we introduce S-MLLMUn Bench, the first benchmark designed to jointly assess the removal of sensitive knowledge and retention of general visual understanding. Extensive experiments show that, unlike prior methods, SMFA achieves precise and controllable unlearning while maintaining the model’s foundational image understanding.

Primary Area: foundation or frontier models, including LLMs

Submission Number: 49