@inproceedings{wang-etal-2026-llms,
    title = "Do {LLM}s Capture Embodied Cognition and Cultural Variation? Cross-Linguistic Evidence from Demonstratives",
    author = "Wang, Yu  and
      Chersoni, Emmanuele  and
      Huang, Chu-Ren",
    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.461/",
    pages = "10158--10174",
    ISBN = "979-8-89176-390-6",
    abstract = "Do large language models (LLMs) truly acquire embodied cognition and cultural conventions from text? We introduce demonstratives, fundamental spatial expressions like ``this/that'' in English and ``这/那'' in Chinese, as a novel probe for grounded knowledge. Using 6,400 responses from 320 native speakers, we establish a human baseline: English speakers reliably distinguish proximal{--}distal referents but struggle with perspective-taking, while Chinese speakers switch perspectives fluently but tolerate distal ambiguity. In contrast, five state-of-the-art LLMs fail to inherently understand the proximal{--}distal contrast and show no cultural differences, defaulting to English-centric reasoning. Our study contributes (i) demonstratives as a new lens for evaluating embodied cognition and cultural conventions, (ii) empirical evidence of cross-cultural asymmetries in human interpretation, (iii) a new perspective on the egocentric{--}sociocentric debate, showing both orientations coexist but vary across languages, and (iv) a call to address individual variation in future model design."
}