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This study investigates how LLMs ground abstract concepts compared to humans, finding a significant 'grounding gap' where models rely heavily on word associations rather than emotional or internal states. Using sparse autoencoders, the authors identify internal features related to grounding dimensions, suggesting LLMs possess this information but do not recruit it naturally during generation.