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The threat of analytic flexibility in using large language models to simulate human data: A call to attention
Social scientists are now using large language models to create “silicon samples” – synthetic datasets intended to stand in for human respondents, aimed at revolutionising human subjects research. However, there are many analytic choices which must be made to produce these samples. Though many of these choices are defensible, their impact on sample quality is poorly understood. I map out these analytic choices and demonstrate how a very small number of decisions can dramatically change the correspondence between silicon samples and human data. Configurations (N = 252) varied substantially in their capacity to estimate (i) rank ordering of participants, (ii) response distributions, and (iii) between-scale correlations. Most critically, configurations were not consistent in quality: those that performed well on one dimension often performed poorly on another, implying that there is no “one-size-fits-all” configuration that optimises the accuracy of these samples. I call for greater attention to the threat of analytic flexibility in using silicon samples.
Date:
21 October 2025, 14:00
Venue:
Manor Road Building, Manor Road OX1 3UQ
Venue Details:
Skills Lab
Speaker:
Jamie Cummins (University of Bern)
Organiser:
Emma Madden (University of Oxford)
Organiser contact email address:
emma.madden@politics.ox.ac.uk
Part of:
Synthetic Social Science Workshop
Booking required?:
Not required
Audience:
Public
Editor:
Emma Madden
