CLEAR: Character Unlearning in Textual and Visual Modalities
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
671a4315a7873ff9b21ce9fb
Alexey Dontsov
extracted_confirmed
2024-10-30T06:58:08.467Z
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Alexey Dontsov
user
therem
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Dmitrii Korzh
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Alexey Zhavoronkin
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Boris Mikheev
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Denis Bobkov
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Aibek Alanov
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2025-06-10T11:21:20.769Z
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Aibek Alanov
user
ai-alanov
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Oleg Y. Rogov
claimed_verified
2024-11-03T14:23:51.344Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/6453df2b908e259483c12bdb/j0QZDDWffeZNiTMEgQTtS.jpeg
Oleg Y. Rogov
user
qubitter
671a4315a7873ff9b21cea02
Ivan Oseledets
claimed_verified
2025-03-25T19:02:47.456Z
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Ivan Oseledets
user
oseledets
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Elena Tutubalina
claimed_verified
2024-10-31T09:55:17.097Z
662f8d645c4db70c77a203b0
/avatars/72f9a3c39b3ba5114388d16a35524835.svg
Elena Tutubalina
user
tlenusik
CLEAR: Character Unlearning in Textual and Visual Modalities
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
CLEAR benchmark evaluates multimodal unlearning methods across textual and visual data, highlighting challenges and demonstrating the effectiveness of $\ell_1$ regularization on LoRA weights in mitigating catastrophic forgetting.
Machine Unlearning MU multimodal language models MLMMs multimodal unlearning MMU CLEAR benchmark fictitious individuals question-answer pairs $\ell_1$ regularization LoRA weights catastrophic forgetting