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| Autores principales: | , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2604.05930 |
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| _version_ | 1866914453810839552 |
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| author | Xu, Naen Sheng, Jiayi Li, Changjiang Zhou, Chunyi Li, Yuyuan Du, Tianyu Wang, Jun Fu, Zhihui Li, Jinbao Ji, Shouling |
| author_facet | Xu, Naen Sheng, Jiayi Li, Changjiang Zhou, Chunyi Li, Yuyuan Du, Tianyu Wang, Jun Fu, Zhihui Li, Jinbao Ji, Shouling |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_05930 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | "I See What You Did There": Can Large Vision-Language Models Understand Multimodal Puns? Xu, Naen Sheng, Jiayi Li, Changjiang Zhou, Chunyi Li, Yuyuan Du, Tianyu Wang, Jun Fu, Zhihui Li, Jinbao Ji, Shouling Computation and Language Artificial Intelligence 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. |
| title | "I See What You Did There": Can Large Vision-Language Models Understand Multimodal Puns? |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2604.05930 |