Reproduce “An Information Theory of Compute-Optimal Size Scaling, Emergence, and Plateaus in Language Models” on OpenResearch

About this paper

Anuj K. Nayak, Lav R. Varshney

Researchers from the University of Illinois at Urbana-Champaign developed a unified mathematical framework leveraging information and random graph theory to explain compute-optimal scaling, emergent capabilities, and performance plateaus in large language models, deriving the Chinchilla rule and predicting multiple plateaus with future emergences.

Key insights

• The compute-optimal Chinchilla scaling rule, dictating equal scaling of model and dataset size with compute, can be derived from first principles through an information-theoretic optimization of concept learning.
• Emergent capabilities in LLMs are explained by a phase transition in a random skill graph, where the formation of a Giant Connected Component enables the composition of a vast majority of skills.
• Performance plateaus are not ultimate limits but rather intermediate stages, indicating the possibility of subsequent emergent phenomena with further scaling if required skills have multimodal distributions.

Summary by alphaXiv.