@article{cheng2026reasoning,title={Reasoning That Travels: Dissecting How Chain-of-Thought Transfers Across Models},author={Cheng, Xinyuan and Chen, B. and Mondorf, P. and Plank, B.},journal={arXiv preprint arXiv:2605.28913},year={2026},url={https://arxiv.org/abs/2605.28913},}
XToM: Exploring the Multilingual Theory of Mind for Large Language Models
Chunkit Chan, Yauwai Yim, Hongchuan Zeng, and 14 more authors
In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Jul 2026
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.
@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}}
Gmg: A video prediction method based on global focus and motion guided
Y. Du, H. Liu, H. Peng, and 3 more authors
IEEE Transactions on Circuits and Systems for Video Technology, 2026
@article{du2026gmg,title={Gmg: A video prediction method based on global focus and motion guided},author={Du, Y. and Liu, H. and Peng, H. and Cheng, Xinyuan and Wu, C. and Zhang, J.},journal={IEEE Transactions on Circuits and Systems for Video Technology},volume={36},number={6},pages={8048--8063},year={2026},doi={10.1109/TCSVT.2026.3657055},url={https://doi.org/10.1109/TCSVT.2026.3657055}}
2025
Predicting sudden stratospheric warmings using video prediction methods
Y. Du, J. Zhang, Xinyuan Cheng, and 3 more authors
@article{du2025ssw,title={Predicting sudden stratospheric warmings using video prediction methods},author={Du, Y. and Zhang, J. and Cheng, Xinyuan and Lu, Y. and Li, D. and Tian, W.},journal={Geophysical Research Letters},volume={52},number={8},pages={e2024GL113993},year={2025},doi={10.1029/2024GL113993},url={https://doi.org/10.1029/2024GL113993}}
2024
Do large language models understand conversational implicature - a case study with a Chinese sitcom
S. Yue, S. Song, Xinyuan Cheng, and 1 more author
In Proceedings of the 23rd Chinese National Conference on Computational Linguistics (Volume 1: Main Conference), 2024
@inproceedings{yue2024implicature,title={Do large language models understand conversational implicature - a case study with a Chinese sitcom},author={Yue, S. and Song, S. and Cheng, Xinyuan and Hu, H.},booktitle={Proceedings of the 23rd Chinese National Conference on Computational Linguistics (Volume 1: Main Conference)},address={Taiyuan, China},publisher={Chinese Information Processing Society of China},year={2024},pages={1270--1285},url={https://aclanthology.org/2024.ccl-1.98/}}
2023
Argugpt: Evaluating, understanding and identifying argumentative essays generated by GPT models
@article{liu2023argugpt,title={Argugpt: Evaluating, understanding and identifying argumentative essays generated by GPT models},author={Liu, Y. and Zhang, Z. and Zhang, W. and Yue, S. and Zhao, X. and Cheng, Xinyuan and Zhang, Y. and Hu, H.},journal={arXiv preprint arXiv:2304.07666},year={2023},url={https://arxiv.org/abs/2304.07666}}