@inproceedings{dontsov-etal-2025-clear,
    title = "{CLEAR}: Character Unlearning in Textual and Visual Modalities",
    author = "Dontsov, Alexey  and
      Korzh, Dmitrii  and
      Zhavoronkin, Alexey  and
      Mikheev, Boris  and
      Bobkov, Denis  and
      Alanov, Aibek  and
      Rogov, Oleg  and
      Oseledets, Ivan  and
      Tutubalina, Elena",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingestion-acl-25/2025.findings-acl.1058/",
    pages = "20582--20603",
    ISBN = "979-8-89176-256-5",
    abstract = "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 [link](https://huggingface.co/datasets/therem/CLEAR)"
}