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    <abstract>The rapid expansion of memes on social media has highlighted the urgent need for effective approaches to detect harmful content. However, traditional data-driven approaches struggle to detect new memes due to their evolving nature and the lack of up-to-date annotated data. To address this issue, we propose MIND, a multi-agent framework for zero-shot harmful meme detection that does not rely on annotated data. MIND implements three key strategies: 1) We retrieve similar memes from an unannotated reference set to provide contextual information. 2) We propose a bi-directional insight derivation mechanism to extract a comprehensive understanding of similar memes. 3) We then employ a multi-agent debate mechanism to ensure robust decision-making through reasoned arbitration. Extensive experiments on three meme datasets demonstrate that our proposed framework not only outperforms existing zero-shot approaches but also shows strong generalization across different model architectures and parameter scales, providing a scalable solution for harmful meme detection.</abstract>
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%0 Conference Proceedings
%T MIND: A Multi-agent Framework for Zero-shot Harmful Meme Detection
%A Liu, Ziyan
%A Fan, Chunxiao
%A Lou, Haoran
%A Wu, Yuexin
%A Deng, Kaiwei
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-251-0
%F liu-etal-2025-mind
%X The rapid expansion of memes on social media has highlighted the urgent need for effective approaches to detect harmful content. However, traditional data-driven approaches struggle to detect new memes due to their evolving nature and the lack of up-to-date annotated data. To address this issue, we propose MIND, a multi-agent framework for zero-shot harmful meme detection that does not rely on annotated data. MIND implements three key strategies: 1) We retrieve similar memes from an unannotated reference set to provide contextual information. 2) We propose a bi-directional insight derivation mechanism to extract a comprehensive understanding of similar memes. 3) We then employ a multi-agent debate mechanism to ensure robust decision-making through reasoned arbitration. Extensive experiments on three meme datasets demonstrate that our proposed framework not only outperforms existing zero-shot approaches but also shows strong generalization across different model architectures and parameter scales, providing a scalable solution for harmful meme detection.
%R 10.18653/v1/2025.acl-long.46
%U https://aclanthology.org/2025.acl-long.46/
%U https://doi.org/10.18653/v1/2025.acl-long.46
%P 923-947

Markdown (Informal)

[MIND: A Multi-agent Framework for Zero-shot Harmful Meme Detection](https://aclanthology.org/2025.acl-long.46/) (Liu et al., ACL 2025)

• MIND: A Multi-agent Framework for Zero-shot Harmful Meme Detection (Liu et al., ACL 2025)

ACL

• Ziyan Liu, Chunxiao Fan, Haoran Lou, Yuexin Wu, and Kaiwei Deng. 2025. MIND: A Multi-agent Framework for Zero-shot Harmful Meme Detection. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 923–947, Vienna, Austria. Association for Computational Linguistics.