  This study uncovers the factor of general intelligence, or g, in language
models, extending the psychometric theory traditionally applied to humans and
certain animal species. Utilizing factor analysis on two extensive datasets -
Open LLM Leaderboard with 1,232 models and General Language Understanding
Evaluation (GLUE) Leaderboard with 88 models - we find compelling evidence for
a unidimensional, highly stable g factor that accounts for 85% of the variance
in model performance. The study also finds a moderate correlation of .49
between model size and g. The discovery of g in language models offers a
unified metric for model evaluation and opens new avenues for more robust,
g-based model ability assessment. These findings lay the foundation for
understanding and future research on artificial general intelligence from a
psychometric perspective and have practical implications for model evaluation
and development.
