@inproceedings{xu-etal-2026-see,
    title = "``{I} See What You Did There'': Can Large Vision-Language Models Understand Multimodal Puns?",
    author = "Xu, Naen  and
      Sheng, Jiayi  and
      Li, Changjiang  and
      Zhou, Chunyi  and
      Li, Yuyuan  and
      Du, Tianyu  and
      Wang, Jun  and
      Fu, Zhihui  and
      Li, Jinbao  and
      Ji, Shouling",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/check-for-anonymous-pdfs/2026.acl-long.444/",
    pages = "9786--9805",
    ISBN = "979-8-89176-390-6",
    abstract = "Puns are a common form of rhetorical wordplay that exploits polysemy and phonetic similarity to create humor. In multimodal puns, visual and textual elements synergize to ground the literal sense and evoke the figurative meaning simultaneously. Although Vision-Language Models (VLMs) are widely used in multimodal understanding and generation, their ability to understand puns has not been systematically studied due to a scarcity of rigorous benchmarks. To address this, we first propose a multimodal pun generation pipeline. We then introduce MultiPun, a dataset comprising diverse types of puns alongside adversarial non-pun distractors. Our evaluation reveals that most models struggle to distinguish genuine puns from these distractors. Moreover, we propose both prompt-level and model-level strategies to enhance pun comprehension, with an average improvement of 16.5{\%} in F1 scores. Our findings provide valuable insights for developing future VLMs that master the subtleties of human-like humor via cross-modal reasoning."
}