@inproceedings{zhang-etal-2025-mc,
    title = "{MC}-{MKE}: A Fine-Grained Multimodal Knowledge Editing Benchmark Emphasizing Modality Consistency",
    author = "Zhang, Junzhe  and
      Zhang, Huixuan  and
      Yin, Xunjian  and
      Huang, Baizhou  and
      Zhang, Xu  and
      Hu, Xinyu  and
      Wan, Xiaojun",
    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/display_plenaries/2025.findings-acl.896/",
    pages = "17430--17445",
    ISBN = "979-8-89176-256-5",
    abstract = "Multimodal large language models (MLLMs) are prone to non-factual or outdated knowledge issues, highlighting the importance of knowledge editing. Many benchmark has been proposed for researching multimodal knowledge editing. However, previous benchmarks focus on limited scenarios due to the lack of rigorous definition of multimodal knowledge. To better evaluate multimodal knowledge editing, we propose a decomposed definition of multimodal knowledge. Following the decomposed definition of multimodal knowledge, we introduce three scenarios and a novel requirement modality consistency. We construct MC-MKE, a fine-grained **M**ultimodal **K**nowledge **E**diting benchmark emphasizing **M**odality **C**onsistency through strict data selection. We evaluate four multimodal knowledge editing methods on MC-MKE, revealing their limitations, particularly in terms of modality consistency. Our work highlights the challenges posed by multimodal knowledge editing and motivates further research in developing effective techniques for this task."
}