CLEAR: Character Unlearning in Textual and Visual Modalities

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 atthis https URL

@article{dontsov2025_2410.18057,
  title={ CLEAR: Character Unlearning in Textual and Visual Modalities },
  author={ Alexey Dontsov and Dmitrii Korzh and Alexey Zhavoronkin and Boris Mikheev and Denis Bobkov and Aibek Alanov and Oleg Y. Rogov and Ivan Oseledets and Elena Tutubalina },
  journal={arXiv preprint arXiv:2410.18057},
  year={ 2025 }
}