CLEAR: Character Unlearning in Textual and Visual Modalities
Authors: name: Alexey Dontsov, name: Dmitrii Korzh, name: Alexey Zhavoronkin, name: Boris Mikheev, name: Denis Bobkov, name: Aibek Alanov, name: Oleg Y. Rogov, name: Ivan Oseledets, name: Elena Tutubalina

Machine Unlearning (MU) is critical for enhancing privacy and security in
deep learning models, particularly in large multimodal language models (MLLMs),
by removing specific private or hazardous information. While MU has made
significant progress in textual and visual modalities, multimodal unlearning
(MMU) remains significantly underexplored, partially due to the absence of a
suitable open-source benchmark. To address this, we introduce CLEAR, a new
benchmark designed to evaluate MMU methods. CLEAR contains 200 fictitious
individuals and 3,700 images linked with corresponding question-answer pairs,
enabling a thorough evaluation across modalities. We assess 10 MU methods,
adapting them for MMU, and highlight new challenges specific to multimodal
forgetting. We also demonstrate that simple ell_1 regularization on LoRA
weights significantly mitigates catastrophic forgetting, preserving model
performance on retained data. The dataset is available at
https://huggingface.co/datasets/therem/CLEAR