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

Qihui Xu (), Yingying Peng, Samuel A. Nastase, Martin Chodorow, Minghua Wu and Ping Li ()
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Qihui Xu: Ohio State University
Yingying Peng: The Hong Kong Polytechnic University
Samuel A. Nastase: Princeton University
Martin Chodorow: Hunter College, City University of New York
Minghua Wu: The Hong Kong Polytechnic University
Ping Li: The Hong Kong Polytechnic University

Nature Human Behaviour, 2025, vol. 9, issue 9, 1871-1886

Abstract: Abstract To what extent can language give rise to complex conceptual representation? Is multisensory experience essential? Recent large language models (LLMs) challenge the necessity of grounding for concept formation: whether LLMs without grounding nevertheless exhibit human-like representations. Here we compare multidimensional representations of ~4,442 lexical concepts between humans (the Glasgow Norms1, N = 829; and the Lancaster Norms2, 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.

Date: 2025
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DOI: 10.1038/s41562-025-02203-8

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