@inproceedings{xu-etal-2026-janusmm,
    title = "{J}anus{MM}: A Benchmark for Self-Deprecation Understanding in Real-World Multimodal Conversations",
    author = "Xu, Xinyi  and
      Hao, Bingguang  and
      Xiong, Yongyi  and
      Chen, Zimo  and
      Liu, Xinchen  and
      Guo, Hongxin  and
      Wang, Xuelong  and
      Zhou, Silin  and
      Dou, Shihan",
    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/ingest-acl/2026.acl-long.1116/",
    pages = "24324--24343",
    ISBN = "979-8-89176-390-6",
    abstract = "Self-deprecation is a prevalent communicative strategy in human society, often using image-text interplay to express emotions and intentions. Despite self-deprecation is widespread in real-world conversations, the ability of multimodal large language models (MLLMs) to understand it remains underexplored. To fill this gap, we introduce **JanusMM**, the first benchmark designed to evaluate MLLMs' understanding of self-deprecation in real-world conversations. JanusMM contains 2,016 bilingual memes from three types of social interactions and provides a dual-task evaluation framework with six new metrics. The first task assesses MLLMs' abilities in self-deprecation recognition and reasoning, while the second task evaluates the consistency of their understanding by simulating the perspectives of the initiator and responder. We evaluate ten frontier MLLMs and find that they exhibit weak recognition and reasoning abilities, with their understanding of self-deprecation remaining inconsistent across both perspectives."
}