@inproceedings{kwon-etal-2026-erase,
title = "Erase Persona, Forget Lore: Benchmarking Multimodal Copyright Unlearning in Large Vision Language Models",
author = "Kwon, June Hyoung and
Yun, Jungmin and
Kim, Youngbin",
editor = "Piperidis, Stelios and
Bel, N{\'u}ria and
van den Heuvel, Henk and
Ide, Nancy and
Krek, Simon and
Toral, Antonio",
booktitle = "Proceedings of the Fifteenth Language Resources and Evaluation Conference",
month = may,
year = "2026",
address = "Palma de Mallorca, Spain",
publisher = "ELRA Language Resource Association",
url = "https://aclanthology.org/2026.lrec-1.727/",
doi = "10.63317/3zvek95uex2j",
pages = "9254--9265",
abstract = "Large Vision-Language Models (LVLMs), trained on web-scale data, risk memorizing and regenerating copyrighted visual content like characters and logos, creating significant challenges. Machine unlearning offers a path to mitigate these risks by removing specific content post-training, but evaluating its effectiveness, especially in the complex multimodal setting of LVLMs, remains an open problem. Current evaluation methods often lack robustness or fail to capture the nuances of cross-modal concept erasure. To address this critical gap, we introduce the CoVUBench benchmark, the first framework specifically designed for evaluating copyright content unlearning in LVLMs. CoVUBench utilizes procedurally generated, legally safe synthetic data coupled with systematic visual variations{---}spanning compositional changes and diverse domain manifestations{---}to ensure realistic and robust evaluation of unlearning generalization. Our comprehensive, multimodal evaluation protocol assesses both forgetting efficacy from the copyright holder{'}s perspective and the preservation of general model utility from the deployer{'}s viewpoint. By rigorously measuring this crucial trade-off, CoVUBench provides a standardized tool to advance the development of responsible and effective unlearning methods for LVLMs."
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<abstract>Large Vision-Language Models (LVLMs), trained on web-scale data, risk memorizing and regenerating copyrighted visual content like characters and logos, creating significant challenges. Machine unlearning offers a path to mitigate these risks by removing specific content post-training, but evaluating its effectiveness, especially in the complex multimodal setting of LVLMs, remains an open problem. Current evaluation methods often lack robustness or fail to capture the nuances of cross-modal concept erasure. To address this critical gap, we introduce the CoVUBench benchmark, the first framework specifically designed for evaluating copyright content unlearning in LVLMs. CoVUBench utilizes procedurally generated, legally safe synthetic data coupled with systematic visual variations—spanning compositional changes and diverse domain manifestations—to ensure realistic and robust evaluation of unlearning generalization. Our comprehensive, multimodal evaluation protocol assesses both forgetting efficacy from the copyright holder’s perspective and the preservation of general model utility from the deployer’s viewpoint. By rigorously measuring this crucial trade-off, CoVUBench provides a standardized tool to advance the development of responsible and effective unlearning methods for LVLMs.</abstract>
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%0 Conference Proceedings
%T Erase Persona, Forget Lore: Benchmarking Multimodal Copyright Unlearning in Large Vision Language Models
%A Kwon, June Hyoung
%A Yun, Jungmin
%A Kim, Youngbin
%Y Piperidis, Stelios
%Y Bel, Núria
%Y van den Heuvel, Henk
%Y Ide, Nancy
%Y Krek, Simon
%Y Toral, Antonio
%S Proceedings of the Fifteenth Language Resources and Evaluation Conference
%D 2026
%8 May
%I ELRA Language Resource Association
%C Palma de Mallorca, Spain
%F kwon-etal-2026-erase
%X Large Vision-Language Models (LVLMs), trained on web-scale data, risk memorizing and regenerating copyrighted visual content like characters and logos, creating significant challenges. Machine unlearning offers a path to mitigate these risks by removing specific content post-training, but evaluating its effectiveness, especially in the complex multimodal setting of LVLMs, remains an open problem. Current evaluation methods often lack robustness or fail to capture the nuances of cross-modal concept erasure. To address this critical gap, we introduce the CoVUBench benchmark, the first framework specifically designed for evaluating copyright content unlearning in LVLMs. CoVUBench utilizes procedurally generated, legally safe synthetic data coupled with systematic visual variations—spanning compositional changes and diverse domain manifestations—to ensure realistic and robust evaluation of unlearning generalization. Our comprehensive, multimodal evaluation protocol assesses both forgetting efficacy from the copyright holder’s perspective and the preservation of general model utility from the deployer’s viewpoint. By rigorously measuring this crucial trade-off, CoVUBench provides a standardized tool to advance the development of responsible and effective unlearning methods for LVLMs.
%R 10.63317/3zvek95uex2j
%U https://aclanthology.org/2026.lrec-1.727/
%U https://doi.org/10.63317/3zvek95uex2j
%P 9254-9265
Markdown (Informal)
[Erase Persona, Forget Lore: Benchmarking Multimodal Copyright Unlearning in Large Vision Language Models](https://aclanthology.org/2026.lrec-1.727/) (Kwon et al., LREC 2026)
ACL