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Do LLMs Capture Embodied Cognition and Cultural Variation? Cross-Linguistic Evidence from Demonstratives

arXiv 2604.25423 ↗First posted 28 Apr 2026cs.CL cs.AIcited by 0

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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.

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References · 14

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
  2. 2022Large Language Models are Zero-Shot Reasonerscited by 4,323
  3. 2021A Survey of Embodied AI: From Simulators to Research Taskscited by 256
  4. 2023Better Zero-Shot Reasoning with Role-Play Promptingcited by 220
  5. 2025FutureSightDrive: Thinking Visually with Spatio-Temporal CoT for Autonomous Drivingcited by 165
  6. 2022Event knowledge in large language models: the gap between the impossible and the unlikelycited by 44
  7. 2025Think-While-Generating: On-the-Fly Reasoning for Personalized Long-Form Generationcited by 29
  8. 2024Question Calibration and Multi-Hop Modeling for Temporal Question Answeringcited by 25
  9. 2024Log Probabilities Are a Reliable Estimate of Semantic Plausibility in Base and Instruction-Tuned Language Modelscited by 24
  10. 2025MTR-Bench: A Comprehensive Benchmark for Multi-Turn Reasoning Evaluationcited by 15
  11. 2025Learning to Substitute Words with Model-based Score Rankingcited by 10
  12. 2025CausalAbstain: Enhancing Multilingual LLMs with Causal Reasoning for Trustworthy Abstentioncited by 10
2 more
  1. 2025DeepResearch-Slice: Bridging the Retrieval-Utilization Gap via Explicit Text Slicingcited by 7
  2. 2026Reason Only When Needed: Efficient Generative Reward Modeling via Model-Internal Uncertaintycited by 6

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