@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://aclanthology.org/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."
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<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.</abstract>
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%0 Conference Proceedings
%T XToM: Exploring the Multilingual Theory of Mind for Large Language Models
%A Chan, Chunkit
%A Yim, Yauwai
%A Zeng, Hongchuan
%A Zou, Zhiying
%A Cheng, Xinyuan
%A Sun, Zhifan
%A Deng, Zheye
%A Chung, Kawai
%A Ao, Yuzhuo
%A Yixiang, Fan
%A Jiayang, Cheng
%A Nie, Ercong
%A Wong, Ginny
%A Schmid, Helmut
%A Schuetze, Hinrich
%A See, Simon
%A Song, Yangqiu
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F chan-etal-2026-xtom
%X 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.
%U https://aclanthology.org/2026.acl-long.805/
%P 17681-17716
Markdown (Informal)
[XToM: Exploring the Multilingual Theory of Mind for Large Language Models](https://aclanthology.org/2026.acl-long.805/) (Chan et al., ACL 2026)
ACL
- Chunkit Chan, Yauwai Yim, Hongchuan Zeng, Zhiying Zou, Xinyuan Cheng, Zhifan Sun, Zheye Deng, Kawai Chung, Yuzhuo Ao, Fan Yixiang, Cheng Jiayang, Ercong Nie, Ginny Wong, Helmut Schmid, Hinrich Schuetze, Simon See, and Yangqiu Song. 2026. XToM: Exploring the Multilingual Theory of Mind for Large Language Models. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 17681–17716, San Diego, California, United States. Association for Computational Linguistics.