  Semantic features have been playing a central role in investigating the
nature of our conceptual representations. Yet the enormous time and effort
required to empirically sample and norm features from human raters has
restricted their use to a limited set of manually curated concepts. Given
recent promising developments with transformer-based language models, here we
asked whether it was possible to use such models to automatically generate
meaningful lists of properties for arbitrary object concepts and whether these
models would produce features similar to those found in humans. To this end, we
probed a GPT-3 model to generate semantic features for 1,854 objects and
compared automatically-generated features to existing human feature norms.
GPT-3 generated many more features than humans, yet showed a similar
distribution in the types of generated features. Generated feature norms
rivaled human norms in predicting similarity, relatedness, and category
membership, while variance partitioning demonstrated that these predictions
were driven by similar variance in humans and GPT-3. Together, these results
highlight the potential of large language models to capture important facets of
human knowledge and yield a new approach for automatically generating
interpretable feature sets, thus drastically expanding the potential use of
semantic features in psychological and linguistic studies.
