Does Conceptual Representation Require Embodiment? Insights From Large Language Models
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Recent advances in large language models (LLM) have the potential to shed light on the debate regarding the extent to which knowledge representation requires the grounding of embodied experience. Despite learning from limited modalities (e.g., text for GPT-3.5, and text+image for GPT-4), LLMs have nevertheless demonstrated human-like behaviors in various psychology tasks, which may provide an alternative interpretation of the acquisition of conceptual knowledge. We compared lexical conceptual representations between humans and ChatGPT (GPT-3.5 and GPT-4) on subjective ratings of various lexical conceptual features or dimensions (e.g., emotional arousal, concreteness, haptic, etc.). The results show that both GPT-3.5 and GPT-4 were strongly correlated with humans in some abstract dimensions, such as emotion and salience. In dimensions related to sensory and motor domains, GPT-3.5 shows weaker correlations while GPT-4 has made significant progress compared to GPT-3.5. Still, GPT-4 struggles to fully capture motor aspects of conceptual knowledge such as actions with foot/leg, mouth/throat, and torso. Moreover, we found that GPT-4's progress can largely be associated with its training in the visual domain. Certain aspects of conceptual representation appear to exhibit a degree of independence from sensory capacities, but others seem to necessitate them. Our findings provide insights into the complexities of knowledge representation from diverse perspectives and highlights the potential influence of embodied experience in shaping language and cognition.
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