Despite the ubiquity of large language models (LLMs) in AI research, the
question of embodiment in LLMs remains underexplored, distinguishing them from
embodied systems in robotics where sensory perception directly informs physical
action. Our investigation navigates the intriguing terrain of whether LLMs,
despite their non-embodied nature, effectively capture implicit human
intuitions about fundamental, spatial building blocks of language. We employ
insights from spatial cognitive foundations developed through early
sensorimotor experiences, guiding our exploration through the reproduction of
three psycholinguistic experiments. Surprisingly, correlations between model
outputs and human responses emerge, revealing adaptability without a tangible
connection to embodied experiences. Notable distinctions include polarized
language model responses and reduced correlations in vision language models.
This research contributes to a nuanced understanding of the interplay between
language, spatial experiences, and the computations made by large language
models. More at this https URL

尽管大型语言模型在人工智能研究中广泛使用，但其在模型具象问题上的探讨仍未得到充分挖掘，这使它们与感知直接影响物理动作的机器人具象系统相区别。本研究通过对人类关于语言基本空间构建模块的内隐直觉是否被大型语言模型有效捕捉来进行了调查。我们借鉴早期感知运动经验中发展的空间认知基础的见解，通过再现三个心理语言学实验来引导我们的探索。令人惊讶的是，模型输出与人类反应之间出现了相关性，揭示了在没有具体联系到具象经验的情况下的适应能力。值得注意的区别包括极化的语言模型反应和视觉语言模型中降低的相关性。本研究对于深入理解语言、空间经验和大型语言模型所进行的计算之间的相互作用做出了贡献。