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Predicting Human Similarity Judgments Using Large Language Models
Raja Marjieh · Ilia Sucholutsky · Theodore R Sumers · Nori Jacoby · Thomas Griffiths · Thomas Griffiths
Event URL: https://openreview.net/forum?id=XGrOjI_xnF »

Similarity judgments provide a well-established method for accessing mental representations, with applications in psychology, neuroscience and machine learning. However, collecting similarity judgments can be prohibitively expensive for naturalistic datasets as the number of comparisons grows quadratically in the number of stimuli. We leverage recent advances in language models and online recruitment, proposing an efficient domain-general procedure for predicting human similarity judgments based on text descriptions. Crucially, the number of descriptions required grows only linearly with the number of stimuli, drastically reducing the amount of data required. We test this procedure on six datasets of naturalistic images and show that our models outperform previous approaches based on visual information.

Author Information

Raja Marjieh (Princeton University)
Ilia Sucholutsky (Princeton University)
Theodore R Sumers (Princeton University)
Nori Jacoby (Max-Planck Institute)
Thomas Griffiths (Princeton University)
Thomas Griffiths (Princeton University)

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