LLMs and the Madness of Crowds
This provides new insights into the underlying structures and relationships among LLMs, which is incremental for understanding model behavior in AI evaluation.
The study analyzed patterns of incorrect answers in large language models (LLMs) and found that errors are systematically correlated across models, revealing non-intuitive behaviors and enabling a taxonomy based on error correlations.
We investigate the patterns of incorrect answers produced by large language models (LLMs) during evaluation. These errors exhibit highly non-intuitive behaviors unique to each model. By analyzing these patterns, we measure the similarities between LLMs and construct a taxonomy that categorizes them based on their error correlations. Our findings reveal that the incorrect responses are not randomly distributed but systematically correlated across models, providing new insights into the underlying structures and relationships among LLMs.