CLEAR: Character Unlearning in Textual and Visual Modalities
Alexey Dontsov, Dmitrii Korzh, Alexey Zhavoronkin, Boris Mikheev, Denis Bobkov, Aibek Alanov, Oleg Rogov, Ivan Oseledets
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)
BibTeX
@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://aclanthology.org/2025.findings-acl.1058/",
doi = "10.18653/v1/2025.findings-acl.1058",
pages = "20582--20603",
ISBN = "979-8-89176-256-5"
}