Do LLMs Capture Embodied Cognition and Cultural Variation? Cross-Linguistic Evidence from Demonstratives
Not on the map. Why?
Abstract (arXiv)
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 "zh`e/n`a" 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) a new task, based on 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.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 14 of its 53 references that are in the map. First posted on arXiv 28 Apr 2026.
Builds on
14 of 53 references.
References · 14
- 2020Language Models are Few-Shot Learnerscited by 32,199
- 2022Large Language Models are Zero-Shot Reasonerscited by 4,323
- 2021A Survey of Embodied AI: From Simulators to Research Taskscited by 256
- 2023Better Zero-Shot Reasoning with Role-Play Promptingcited by 220
- 2025FutureSightDrive: Thinking Visually with Spatio-Temporal CoT for Autonomous Drivingcited by 165
- 2022Event knowledge in large language models: the gap between the impossible and the unlikelycited by 44
- 2025Think-While-Generating: On-the-Fly Reasoning for Personalized Long-Form Generationcited by 29
- 2024Question Calibration and Multi-Hop Modeling for Temporal Question Answeringcited by 25
- 2024Log Probabilities Are a Reliable Estimate of Semantic Plausibility in Base and Instruction-Tuned Language Modelscited by 24
- 2025MTR-Bench: A Comprehensive Benchmark for Multi-Turn Reasoning Evaluationcited by 15
- 2025Learning to Substitute Words with Model-based Score Rankingcited by 10
- 2025CausalAbstain: Enhancing Multilingual LLMs with Causal Reasoning for Trustworthy Abstentioncited by 10
Built on by
Not cited yet.