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Building on research arguing for the possibility of conceptual andcategorical knowledge acquisition through statistics contained in language, weevaluate predictive language models (LMs) -- informed solely by textual input-- on a prevalent phenomenon in cognitive science: typicality. Inspired byexperiments that involve language processing and show robust typicality effectsin humans, we propose two tests for LMs. Our first test targets whethertypicality modulates LM probabilities in assigning taxonomic categorymemberships to items. The second test investigates sensitivities to typicalityin LMs' probabilities when extending new information about items to theircategories. Both tests show modest -- but not completely absent --correspondence between LMs and humans, suggesting that text-based exposurealone is insufficient to acquire typicality knowledge.

Do language models learn typicality judgments from text?