EnCORE

Extrapolating scaling trends suggest that training dataset size for LLMs may soon be limited by the amount of text data available on the internet. In this talk we investigate scaling language models in data-constrained regimes. Specifically, we run a set of empirical experiments varying the extent of data repetition and compute budget. From these experiments we propose and empirically validate a scaling law for compute optimality that accounts for the decreasing value of repeated tokens and excess parameters. Finally, we discuss and experiment with approaches for mitigating data scarcity.

ABSTRACT The advent of deep learning has completely revolutionized how we perceive data to obtain superhuman performance across all fields of modern science. However, despite the remarkable empirical successes of deep learners, the theoretical guarantees for their statistical accuracy remain rather pessimistic. In particular, the data distributions on which deep learners are generally applied, such […]