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XToM: Exploring the Multilingual Theory of Mind for Large Language Models

MCML Authors

Link to Profile Hinrich Schütze

Hinrich Schütze

Prof. Dr.

Core PI

Computational Linguistics

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, which is 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.

inproceedings CYZ+26

Findings @ACL 2026

Findings at the 64th Annual Meeting of the Association for Computational Linguistics. San Diego, CA, USA, Jul 02-07, 2026. To be published. Preprint available.
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Authors

C. Chan • Y. Yim • H. Zeng • Z. Zou • X. Cheng • Z. Sun • Z. Deng • K. Chung • Y. Ao • Y. Fan • C. Jiayang • E. Nie • G. Y. Wong • H. Schmid • H. Schütze • S. See • Y. Song

Links

arXiv

In Collaboration

 NVIDIA

Research Area

 B2 | Natural Language Processing

BibTeXKey: CYZ+26

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