This episode breaks down the 'Scaling Laws for Neural Language Models' research paper, which investigates scaling laws for neural language models, particularly Transformer models. The authors explore how model performance is influenced by factors such as model size, dataset size, and the amount of compute used for training. They observe precise power-law relationships between these factors and performance, suggesting that language modelling performance improves smoothly and predictably as these factors are appropriately scaled up. Notably, the authors find that larger models are significantly more sample-efficient and that optimal compute-efficient training involves training very large models on a relatively modest amount of data and stopping before convergence.

Audio : (Spotify) https://open.spotify.com/episode/2mi7pD3fLZ20eREVPecZXh?si=tYYgtafWRzC0lneHcfN2ZQ

Paper: https://arxiv.org/abs/2001.08361