🤖 AI Summary
Current evaluations of large language models often conflate benchmark scores with true capabilities, overlooking issues such as test set contamination and annotation errors, thereby undermining construct validity. This work proposes a Structured Capability Model that, for the first time, jointly models the influence of model scale on intrinsic capabilities and the distortion introduced by measurement error within a unified framework, enabling quantitative assessment of evaluation construct validity. By integrating the strengths of scaling laws and latent factor models, our approach extracts interpretable and generalizable latent capability indicators from massive benchmark data. Evaluated on the OpenLLM Leaderboard, the proposed model demonstrates superior parsimonious fit compared to standard latent factor models and outperforms scaling laws in out-of-distribution benchmark prediction, exhibiting enhanced explanatory and predictive power.
📝 Abstract
The LLM community often reports benchmark results as if they are synonymous with general model capabilities. However, benchmarks can have problems that distort performance, like test set contamination and annotator error. How can we know that a benchmark is a reliable indicator of some capability that we want to measure? This question concerns the construct validity of LLM benchmarks, and it requires separating benchmark results from capabilities when we model and predict LLM performance.
Both social scientists and computer scientists propose formal models - latent factor models and scaling laws - for identifying the capabilities underlying benchmark scores. However, neither technique is satisfactory for construct validity. Latent factor models ignore scaling laws, and as a result, the capabilities they extract often proxy model size. Scaling laws ignore measurement error, and as a result, the capabilities they extract are both uninterpretable and overfit to the observed benchmarks.
This thesis presents the structured capabilities model, the first model to extract interpretable and generalisable capabilities from a large collection of LLM benchmark results. I fit this model and its two alternatives on a large sample of results from the OpenLLM Leaderboard. Structured capabilities outperform latent factor models on parsimonious fit indices, and exhibit better out-of-distribution benchmark prediction than scaling laws. These improvements are possible because neither existing approach separates model scale from capabilities in the appropriate way. Model scale should inform capabilities, as in scaling laws, and these capabilities should inform observed results up to measurement error, as in latent factor models. In combining these two insights, structured capabilities demonstrate better explanatory and predictive power for quantifying construct validity in LLM evaluations.