@inproceedings{xu-etal-2025-punmemecn,
    title = "{P}un{M}eme{CN}: A Benchmark to Explore Vision-Language Models' Understanding of {C}hinese Pun Memes",
    author = "Xu, Zhijun  and
      Yuan, Siyu  and
      Zhang, Yiqiao  and
      Sun, Jingyu  and
      Zheng, Tong  and
      Yang, Deqing",
    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://preview.aclanthology.org/ingest-emnlp/2025.emnlp-main.944/",
    pages = "18705--18721",
    ISBN = "979-8-89176-332-6",
    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."
}