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    <abstract>Pun memes, which combine wordplay with visual elements, represent a popular form of humor in Chinese online communications. Despite their prevalence, current Vision-Language Models (VLMs) lack systematic evaluation in understanding and applying these culturally-specific multimodal expressions. In this paper, we introduce PunMemeCN, a novel benchmark designed to assess VLMs’ capabilities in processing Chinese pun memes across three progressive tasks: pun meme detection, sentiment analysis, and chat-driven meme response. PunMemeCN consists of 1,959 Chinese memes (653 pun memes and 1,306 non-pun memes) with comprehensive annotations of punchlines, sentiments, and explanations, alongside 2,008 multi-turn chat conversations incorporating these memes. Our experiments indicate that state-of-the-art VLMs struggle with Chinese pun memes, particularly with homophone wordplay, even with Chain-of-Thought prompting. Notably, punchlines in memes can effectively conceal potentially harmful content from AI detection. These findings underscore the challenges in cross-cultural multimodal understanding and highlight the need for culture-specific approaches to humor comprehension in AI systems.</abstract>
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%0 Conference Proceedings
%T PunMemeCN: A Benchmark to Explore Vision-Language Models’ Understanding of Chinese Pun Memes
%A Xu, Zhijun
%A Yuan, Siyu
%A Zhang, Yiqiao
%A Sun, Jingyu
%A Zheng, Tong
%A Yang, Deqing
%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 xu-etal-2025-punmemecn
%X Pun memes, which combine wordplay with visual elements, represent a popular form of humor in Chinese online communications. Despite their prevalence, current Vision-Language Models (VLMs) lack systematic evaluation in understanding and applying these culturally-specific multimodal expressions. In this paper, we introduce PunMemeCN, a novel benchmark designed to assess VLMs’ capabilities in processing Chinese pun memes across three progressive tasks: pun meme detection, sentiment analysis, and chat-driven meme response. PunMemeCN consists of 1,959 Chinese memes (653 pun memes and 1,306 non-pun memes) with comprehensive annotations of punchlines, sentiments, and explanations, alongside 2,008 multi-turn chat conversations incorporating these memes. Our experiments indicate that state-of-the-art VLMs struggle with Chinese pun memes, particularly with homophone wordplay, even with Chain-of-Thought prompting. Notably, punchlines in memes can effectively conceal potentially harmful content from AI detection. These findings underscore the challenges in cross-cultural multimodal understanding and highlight the need for culture-specific approaches to humor comprehension in AI systems.
%R 10.18653/v1/2025.emnlp-main.944
%U https://aclanthology.org/2025.emnlp-main.944/
%U https://doi.org/10.18653/v1/2025.emnlp-main.944
%P 18705-18721

Markdown (Informal)

[PunMemeCN: A Benchmark to Explore Vision-Language Models’ Understanding of Chinese Pun Memes](https://aclanthology.org/2025.emnlp-main.944/) (Xu et al., EMNLP 2025)

• PunMemeCN: A Benchmark to Explore Vision-Language Models’ Understanding of Chinese Pun Memes (Xu et al., EMNLP 2025)

ACL

• Zhijun Xu, Siyu Yuan, Yiqiao Zhang, Jingyu Sun, Tong Zheng, and Deqing Yang. 2025. PunMemeCN: A Benchmark to Explore Vision-Language Models’ Understanding of Chinese Pun Memes. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pages 18705–18721, Suzhou, China. Association for Computational Linguistics.