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Neural network models of language have long been used as a tool fordeveloping hypotheses about conceptual representation in the mind and brain.For many years, such use involved extracting vector-space representations ofwords and using distances among these to predict or understand human behaviorin various semantic tasks. Contemporary large language models (LLMs), however,make it possible to interrogate the latent structure of conceptualrepresentations using experimental methods nearly identical to those commonlyused with human participants. The current work utilizes three common techniquesborrowed from cognitive psychology to estimate and compare the structure ofconcepts in humans and a suite of LLMs. In humans, we show that conceptualstructure is robust to differences in culture, language, and method ofestimation. Structures estimated from LLM behavior, while individually fairlyconsistent with those estimated from human behavior, vary much more dependingupon the particular task used to generate responses--across tasks, estimates ofconceptual structure from the very same model cohere less with one another thando human structure estimates. These results highlight an important differencebetween contemporary LLMs and human cognition, with implications forunderstanding some fundamental limitations of contemporary machine language.

Conceptual structure coheres in human cognition but not in large language models