Research Reveals that Sensory and Motor Inputs Help Large Language Models Represent Complex Concepts

By exploring the similarities between Large Language Models (LLMs) and human representations, researchers at PolyU led by Faculty Dean Prof. Li Ping and their collaborators have shed new light on the extent to which language alone can shape the formation and learning of complex conceptual knowledge. Their findings also revealed how the use of sensory input for grounding or embodiment – connecting abstract with concrete concepts during learning – affects the ability of LLMs to understand complex concepts and form human-like representations. The study, in collaboration with scholars from Ohio State University, Princeton University and City University of New York, was recently published in Nature Human Behaviour. Click HERE to read the article.