Scaling Laws for Neural Language Models

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Scaling Laws for Neural Language Models

Abstract

We study empirical scaling laws for language model performance on the cross-entropy loss. The loss scales as a power-law with model size, dataset size, and the amount of compute used for training, with some trends spanning more than seven orders of magnitude. Other architectural details such as network width or depth have minimal effects within a wide range. Simple equations govern the dependence of overfitting on model/dataset size and the dependence of training speed on model size. These relationships allow us to determine the optimal allocation of a fixed compute budget. Larger models are significantly more sample-efficient, such that optimally compute-efficient training involves training very large models on a relatively modest amount of data and stopping significantly before convergence.

One Introduction

Language provides a natural domain for the study of artificial intelligence, as the vast majority of reasoning tasks can be efficiently expressed and evaluated in language, and the world's text provides a wealth of data for unsupervised learning via generative modeling. Deep learning has recently seen rapid progress in language modeling, with state of the art models approaching human-level performance on many specific tasks, including the composition of coherent multi-paragraph prompted text samples.

One might expect language modeling performance to depend on model architecture, the size of neural models, the computing power used to train them, and the data available for this training process. In this work we will empirically investigate the dependence of language modeling loss on all of these factors, focusing on the Transformer architecture. The high ceiling and low floor for performance on language tasks allows us to study trends over more than seven orders of magnitude in scale.

Throughout we will observe precise power-law scalings for performance as a function of training time, context length, dataset size, model size, and compute budget.

One point one Summary

One point two Summary of Scaling Laws

One point three. Notation

Two. Background and Methods

Two point one. Parameter and Compute Scaling of Transformers

Two point two. Training Procedures

Two point three. Datasets

Three. Empirical Results and Basic Power Laws

Three point one. Approximate Transformer Shape and Hyperparameter Independence

Three point two point one Comparing to LSTMs and Universal Transformers

Three point two point two Generalization Among Data Distributions

Three point three Performance with Dataset Size and Compute

Four Charting the Infinite Data Limit and Overfitting

Four point one Proposed L of N, D Equation

Four point two Results

Five Scaling Laws with Model Size and Training Time

Five point one Adjustment for Training at Berit

Five point two Results for L (N, Smin) and Performance with Model Size and Compute

Five point three Lower Bound on Early Stopping Step

Six Optimal Allocation of the Compute Budget

Six point one Optimal Performance and Allocations

Six point two Predictions from

Six point three. Contradictions and a Conjecture

Seven Related Work

Eight Discussion

A Summary of Power Laws

B Empirical Model of Compute-Efficient Frontier

B point one Defining Equations

B. two Efficient Training

B. three Comparison to Inefficient

B. four Suboptimal Model Sizes

C Caveats

D Supplemental Figures

D point two Universal Transformers

D point three Batch Size

D point four Sample Efficiency versus Model Size

D point five Context Dependence

D point six Learning Rate Schedules and Error Analysis

D point seven Fit Details and Power Law Quality

D point eight Generalization and Architecture

Overview

The study explores how language modeling performance can be predicted based on scaling factors such as model size, dataset size, and training compute. It reveals that larger models and the appropriate scaling of data lead to improved sample efficiency and optimal training outcomes.

Key Points

1Performance on language models strongly correlates with scale, particularly model parameters, dataset size, and compute
2Overfitting improves predictably when scaling datasets in tandem with model size
3Large models demonstrate higher sample efficiency and require fewer data points to achieve optimal performance
4The critical batch size for training is determined by the loss's gradient noise scale.

Details

Authors: Jared Kaplan, Sam McCandlish, Tom Henighan, Tom B. Brown, Benjamin Chess, Rewon Child, Scott Gray, Alec Radford, Jeffrey Wu, Dario Amodei
Category: Technology and Engineering

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