@inproceedings{ryan-etal-2025-humor,
    title = "Humor in Pixels: Benchmarking Large Multimodal Models Understanding of Online Comics",
    author = "Ryan, Yuriel  and
      Tan, Rui Yang  and
      Choo, Kenny Tsu Wei  and
      Lee, Roy Ka-Wei",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/name-variant-enfa-fane/2025.findings-emnlp.755/",
    doi = "10.18653/v1/2025.findings-emnlp.755",
    pages = "14024--14050",
    ISBN = "979-8-89176-335-7",
    abstract = "Understanding humor is a core aspect of social intelligence, yet it remains a significant challenge for Large Multimodal Models (LMMs). We introduce PixelHumor, a benchmark dataset of 2,800 annotated multi-panel comics designed to evaluate LMMs' ability to interpret multimodal humor and recognize narrative sequences. Experiments with state-of-the-art LMMs reveal substantial gaps: for instance, top models achieve only 61{\%} accuracy in panel sequencing, far below human performance. This underscores critical limitations in current models' integration of visual and textual cues for coherent narrative and humor understanding. By providing a rigorous framework for evaluating multimodal contextual and narrative reasoning, PixelHumor aims to drive the development of LMMs that better engage in natural, socially aware interactions."
}