@inproceedings{zhou-etal-2025-m2edit,
title = "{M}2{E}dit: Locate and Edit Multi-Granularity Knowledge in Multimodal Large Language Model",
author = "Zhou, Yang and
Cao, Pengfei and
Chen, Yubo and
Liu, Qingbin and
Sui, Dianbo and
Chen, Xi and
Liu, Kang and
Zhao, Jun",
editor = "Christodoulopoulos, Christos and
Chakraborty, Tanmoy and
Rose, Carolyn and
Peng, Violet",
booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.emnlp-main.1478/",
pages = "29017--29030",
ISBN = "979-8-89176-332-6",
abstract = "Multimodal knowledge editing is an important method for modifying outdated or incorrect knowledge in Multimodal Large Language Models (MLLMs). However, existing datasets for multimodal knowledge editing lack multi-granularity knowledge. In this paper, we present a more realistic dataset called M2Edit, which includes three distinct types of knowledge: entity, relation, and action. Additionally, existing knowledge editing methods for MLLMs lack the ability to handle multi-granularity knowledge and generalize to multimodal data. To address these limitations, we propose the multimodal knowledge editing method MLE. This approach identifies key knowledge layers within different components and collaboratively edits the various components of MLLMs. As a result, we observe significant improvements in visual generality performance, ranging from 4.8 to 10.8, and achieve the best overall performance on knowledge data of different granularities."
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<abstract>Multimodal knowledge editing is an important method for modifying outdated or incorrect knowledge in Multimodal Large Language Models (MLLMs). However, existing datasets for multimodal knowledge editing lack multi-granularity knowledge. In this paper, we present a more realistic dataset called M2Edit, which includes three distinct types of knowledge: entity, relation, and action. Additionally, existing knowledge editing methods for MLLMs lack the ability to handle multi-granularity knowledge and generalize to multimodal data. To address these limitations, we propose the multimodal knowledge editing method MLE. This approach identifies key knowledge layers within different components and collaboratively edits the various components of MLLMs. As a result, we observe significant improvements in visual generality performance, ranging from 4.8 to 10.8, and achieve the best overall performance on knowledge data of different granularities.</abstract>
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%0 Conference Proceedings
%T M2Edit: Locate and Edit Multi-Granularity Knowledge in Multimodal Large Language Model
%A Zhou, Yang
%A Cao, Pengfei
%A Chen, Yubo
%A Liu, Qingbin
%A Sui, Dianbo
%A Chen, Xi
%A Liu, Kang
%A Zhao, Jun
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-332-6
%F zhou-etal-2025-m2edit
%X Multimodal knowledge editing is an important method for modifying outdated or incorrect knowledge in Multimodal Large Language Models (MLLMs). However, existing datasets for multimodal knowledge editing lack multi-granularity knowledge. In this paper, we present a more realistic dataset called M2Edit, which includes three distinct types of knowledge: entity, relation, and action. Additionally, existing knowledge editing methods for MLLMs lack the ability to handle multi-granularity knowledge and generalize to multimodal data. To address these limitations, we propose the multimodal knowledge editing method MLE. This approach identifies key knowledge layers within different components and collaboratively edits the various components of MLLMs. As a result, we observe significant improvements in visual generality performance, ranging from 4.8 to 10.8, and achieve the best overall performance on knowledge data of different granularities.
%U https://aclanthology.org/2025.emnlp-main.1478/
%P 29017-29030
Markdown (Informal)
[M2Edit: Locate and Edit Multi-Granularity Knowledge in Multimodal Large Language Model](https://aclanthology.org/2025.emnlp-main.1478/) (Zhou et al., EMNLP 2025)
ACL
- Yang Zhou, Pengfei Cao, Yubo Chen, Qingbin Liu, Dianbo Sui, Xi Chen, Kang Liu, and Jun Zhao. 2025. M2Edit: Locate and Edit Multi-Granularity Knowledge in Multimodal Large Language Model. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pages 29017–29030, Suzhou, China. Association for Computational Linguistics.