@inproceedings{chan-etal-2026-xtom,
    title = "{XT}o{M}: Exploring the Multilingual Theory of Mind for Large Language Models",
    author = "Chan, Chunkit  and
      Yim, Yauwai  and
      Zeng, Hongchuan  and
      Zou, Zhiying  and
      Cheng, Xinyuan  and
      Sun, Zhifan  and
      Deng, Zheye  and
      Chung, Kawai  and
      Ao, Yuzhuo  and
      Yixiang, Fan  and
      Jiayang, Cheng  and
      Nie, Ercong  and
      Wong, Ginny  and
      Schmid, Helmut  and
      Schuetze, Hinrich  and
      See, Simon  and
      Song, Yangqiu",
    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.805/",
    pages = "17681--17716",
    ISBN = "979-8-89176-390-6",
    abstract = "Theory of Mind (ToM){---}the ability to infer mental states in others{---}is pivotal for human social cognition. Existing evaluations of ToM in LLMs are largely limited to English, neglecting the linguistic diversity that shapes human cognition. This limitation raises a critical question: can LLMs exhibit Multilingual Theory of Mind{---}the capacity to reason about mental states across diverse linguistic contexts? To address this gap, we present XToM, a rigorously validated multilingual benchmark that evaluates ToM across five languages and incorporates diverse, contextually rich task scenarios. Using XToM, we systematically evaluate LLMs (e.g., DeepSeek R1), revealing a pronounced dissonance: while models excel in multilingual language understanding, their ToM performance varies across languages. Our findings expose limitations in LLMs' ability to replicate human-like mentalizing across linguistic contexts."
}