Large language models without grounding recover non-sensorimotor but not sensorimotor features of human concepts

"Here we compare multidimensional representations of ~4,442 lexical concepts between humans (the Glasgow Norms, N = 829; and the Lancaster Norms, N = 3,500) and state-of-the-art LLMs with and without visual learning, across non-sensorimotor, sensory and motor domains. 

"We found that 
  1. The similarity between model and human representations decreases from non-sensorimotor to sensory domains and is minimal in motor domains, indicating a systematic divergence, and 
  2. Models with visual learning exhibit enhanced similarity with human representations in visual-related dimensions. 
"These results highlight the potential limitations of language in isolation for LLMs and that the integration of diverse modalities can potentially enhance alignment with human conceptual representation."

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