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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

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 · ORCID
classification cs.CVcs.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 4 Pith papers

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

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

  2. Towards Benign Memory Forgetting for Selective Multimodal Large Language Model Unlearning

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    An MLLM unlearning method and benchmark that aim to erase targeted private facts while preserving image understanding.

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

    cs.LG 2026-07 conditional novelty 5.0 of 10

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

  4. Towards AI-$45^{\circ}$ Law: A Roadmap to Trustworthy AGI

    cs.CY 2024-12 conditional novelty 4.0 of 10

    The paper proposes the AI-45 degree law, a Causal Ladder framework, and five trustworthiness levels as a roadmap toward trustworthy AGI.

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