大语言模型能捕捉具身认知和文化差异吗？来自跨语言证据的示范词研究
Do LLMs Capture Embodied Cognition and Cultural Variation? Cross-Linguistic Evidence from Demonstratives
AI总结：
本文通过示范词研究探讨大语言模型对具身认知和文化差异的捕捉能力，发现人类在视角转换和距离判断上存在文化差异，而LLMs则缺乏这种理解且默认英语中心化推理。
摘要：
大语言模型（LLMs）真正通过文本获取具身认知和文化惯例吗？我们引入示范词，如英语中的"this/that"和中文中的"zhè/nà"，作为评估 grounded knowledge 的新探针。通过320名母语者的6400份回应，我们建立人类基准：英语使用者能可靠区分近距-远距指代，但难以视角转换；中文使用者能流畅转换视角但容忍远距模糊。相比之下，五种最先进的LLMs无法内在理解近距-远距对比，且无文化差异，默认英语中心化推理。本研究贡献（i）基于示范词的新任务作为评估具身认知和文化惯例的新视角；（ii）人类跨文化解释的不对称性实证；（iii）对自我中心-社会中心辩论的新见解，显示两种取向共存但因语言而异；（iv）呼吁未来模型设计考虑个体差异。
英文摘要：
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 "zhè/nà" 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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
