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CLEAR: Character Unlearning in Textual and Visual Modalities

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arxiv 2410.18057 v4 pith:H5VXPQBV submitted 2024-10-23 cs.CV cs.CL

CLEAR: Character Unlearning in Textual and Visual Modalities

Alexey Dontsov , Dmitrii Korzh , Alexey Zhavoronkin , Boris Mikheev , Denis Bobkov , Aibek Alanov , Oleg Y. Rogov , Ivan Oseledets
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Elena Tutubalina
This is my paper
classification cs.CV cs.CL
keywords clearunlearningmodalitiesacrossevaluationaddressadvancedanalysis
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Machine Unlearning (MU) is critical for removing private or hazardous information from deep learning models. While MU has advanced significantly in unimodal (text or vision) settings, multimodal unlearning (MMU) remains underexplored due to the lack of open benchmarks for evaluating cross-modal data removal. To address this gap, we introduce CLEAR, the first open-source benchmark designed specifically for MMU. CLEAR contains 200 fictitious individuals and 3,700 images linked with corresponding question-answer pairs, enabling a thorough evaluation across modalities. We conduct a comprehensive analysis of 11 MU methods (e.g., SCRUB, gradient ascent, DPO) across four evaluation sets, demonstrating that jointly unlearning both modalities outperforms single-modality approaches. The dataset is available at https://huggingface.co/datasets/therem/CLEAR

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Cited by 3 Pith papers

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  1. Erase Persona, Forget Lore: Benchmarking Multimodal Copyright Unlearning in Large Vision Language Models

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  2. POPS: Recovering Unlearned Multi-Modality Knowledge in MLLMs with Prompt-Optimized Parameter Shaking

    cs.CR 2026-07 conditional novelty 6.0

    Prompt-optimized suffixes plus synthetic fine-tuning recover ~82% of knowledge that multimodal unlearning methods claim to erase from MLLMs.

  3. Multimodal Unlearning Across Vision, Language, Video, and Audio: Survey of Methods, Datasets, and Benchmarks

    cs.LG 2026-07 conditional novelty 5.0

    A system-first taxonomy and literature synthesis of multimodal unlearning across vision, language, video, and audio, with datasets, benchmarks, metrics, applications, and open challenges.