title: CLEAR: Character Unlearning in Textual and Visual Modalities
summary: 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
paper_authors._id: 671a4315a7873ff9b21ce9fb
paper_authors.name: Alexey Dontsov
paper_authors.status: extracted_confirmed
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paper_authors.avatarUrl: /avatars/42fcc5a4b14550b7174b8417d3915709.svg
paper_authors.fullname: Alexey Dontsov
paper_authors.type: user
paper_authors.user: therem
paper_authors._id: 671a4315a7873ff9b21ce9fc
paper_authors.name: Dmitrii Korzh
paper_authors._id: 671a4315a7873ff9b21ce9fd
paper_authors.name: Alexey Zhavoronkin
paper_authors._id: 671a4315a7873ff9b21ce9fe
paper_authors.name: Boris Mikheev
paper_authors._id: 671a4315a7873ff9b21ce9ff
paper_authors.name: Denis Bobkov
paper_authors._id: 671a4315a7873ff9b21cea00
paper_authors.name: Aibek Alanov
paper_authors._id: 671a4315a7873ff9b21cea01
paper_authors.name: Oleg Y. Rogov
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-11-03T14:23:51.344Z
paper_authors._id: 6453df2b908e259483c12bdb
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/6453df2b908e259483c12bdb/j0QZDDWffeZNiTMEgQTtS.jpeg
paper_authors.fullname: Oleg Rogov
paper_authors.type: user
paper_authors.user: qubitter
paper_authors._id: 671a4315a7873ff9b21cea02
paper_authors.name: Ivan Oseledets
paper_authors._id: 671a4315a7873ff9b21cea03
paper_authors.name: Elena Tutubalina
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-10-31T09:55:17.097Z
paper_authors._id: 662f8d645c4db70c77a203b0
paper_authors.avatarUrl: /avatars/72f9a3c39b3ba5114388d16a35524835.svg
paper_authors.fullname: Elena Tutubalina
paper_authors.type: user
paper_authors.user: tlenusik
paper_title: CLEAR: Character Unlearning in Textual and Visual Modalities
paper_summary: 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
paper_ai_keywords: Machine Unlearning (MU) multimodal language models (MLMs) multimodal unlearning (MMU) CLEAR fictitious individuals question-answer pairs $\ell_1$ regularization LoRA weights catastrophic forgetting