Revealing the structure of language model capabilities
Ryan Burnell Han Hao Andrew R. A. Conway Jose Hernandez Orallo
Building a theoretical understanding of the capabilities of large language
models (LLMs) is vital for our ability to predict and explain the behavior of
these systems. Here, we investigate the structure of LLM capabilities by
extracting latent capabilities from patterns of individual differences across a
varied population of LLMs. Using a combination of Bayesian and frequentist
factor analysis, we analyzed data from 29 different LLMs across 27 cognitive
tasks. We found evidence that LLM capabilities are not monolithic. Instead,
they are better explained by three well-delineated factors that represent
reasoning, comprehension and core language modeling. Moreover, we found that
these three factors can explain a high proportion of the variance in model
performance. These results reveal a consistent structure in the capabilities of
different LLMs and demonstrate the multifaceted nature of these capabilities.
We also found that the three abilities show different relationships to model
properties such as model size and instruction tuning. These patterns help
refine our understanding of scaling laws and indicate that changes to a model
that improve one ability might simultaneously impair others. Based on these
findings, we suggest that benchmarks could be streamlined by focusing on tasks
that tap into each broad model ability.