Breaking Myths in LLM scaling and emergent abilities with a comprehensive statistical analysis

Amidst the rapid evolution of LLMs, the significance of evaluation in comprehending and propelling these models forward is increasingly paramount. Evaluations have revealed that factors such as scaling, training types, architectures and other factors profoundly impact the performance of LLMs. However, the extent and nature of these impacts continue to be subjects of debate because most assessments have been restricted to a limited number of models and data points. Clarifying the effects of these factors on performance scores can be more effectively achieved through a statistical lens. Our study embarks on a thorough re-examination of these LLMs, targeting the inadequacies in current evaluation methods. Building on a unified evaluation framework, our study employs two datasets of LLM evaluation results from the same platform to enable temporal cross-validation and introduce a comprehensive multi-facet statistical methodology. This multifaceted statistical framework employs ANOVA, Tukey HSD tests, GAMMs, and clustering analysis, providing a robust and transparent approach to investigating LLM performance data. Contrary to prevailing findings, our results challenge common assumptions about scaling laws, emergent abilities, and the influence of specific training types and architectures in LLMs. These findings furnish new perspectives on the characteristics, intrinsic nature, and developmental trends of these models. By introducing straightforward and reliable methods for analyzing LLM performance data, this study provides both a practical approach to evaluating model performance and a distinct perspective on LLM training dynamics and core capabilities.