Deriving Neural Scaling Laws from the statistics of natural language
This provides the first quantitative theory for neural scaling laws in LLMs, addressing a key gap for researchers and practitioners in large-scale machine learning.
The authors derive a theory that predicts data-limited neural scaling exponents from first principles using two statistical properties of natural language, achieving a remarkable match with experimental scaling laws from GPT-2 and LLaMA models on TinyStories and WikiText.
Despite the fact that experimental neural scaling laws have substantially guided empirical progress in large-scale machine learning, no existing theory can quantitatively predict the exponents of these important laws for any modern LLM trained on any natural language dataset. We provide the first such theory in the case of data-limited scaling laws. We isolate two key statistical properties of language that alone can predict neural scaling exponents: (i) the decay of pairwise token correlations with time separation between token pairs, and (ii) the decay of the next-token conditional entropy with the length of the conditioning context. We further derive a simple formula in terms of these statistics that predicts data-limited neural scaling exponents from first principles without any free parameters or synthetic data models. Our theory exhibits a remarkable match with experimentally measured neural scaling laws obtained from training GPT-2 and LLaMA style models from scratch on two qualitatively different benchmarks, TinyStories and WikiText.