Latent Performance Profiling of Large Language Models
Latent Performance Profiling (LPP) is a framework that assesses large language models by examining their internal workings rather than just benchmark scores. It measures hidden activations to derive task-agnostic diagnostics like predictive uncertainty and representational compression. This reveals fundamental, scale-independent traits, allowing for interpretable comparisons between models with similar external performance.