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Title: Correlated Errors in Large Language Models
Diversity in training data, architecture, and providers is assumed to mitigate homogeneity in LLMs. However, we lack empirical evidence on whether different LLMs differ \textit{meaningfully}. We conduct a large-scale empirical evaluation on over 350 LLMs overall, using two popular leaderboards and a resume-screening task. We find substantial correlation in model errors---on one leaderboard dataset, models agree 60% of the time when both models err. We identify factors driving model correlation, including shared architectures and providers. Crucially, however, larger and more accurate models have highly correlated errors, even with distinct architectures and providers. Finally, we show the effects of correlation in two downstream tasks: LLM-as-judge evaluation and hiring---the latter reflecting theoretical predictions regarding algorithmic monoculture.  more » « less
Award ID(s):
2339427
PAR ID:
10616986
Author(s) / Creator(s):
; ; ;
Publisher / Repository:
International Conference on Machine Learning
Date Published:
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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