Language-model scaling becomes measurable

Empirical relationships connected language-model loss with parameters, data, and compute.

Jared Kaplan and colleagues

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The contribution

The authors measured how language-model performance varied with model size, dataset size, and training compute. Approximate scaling relationships offered a way to reason about resource allocation within the studied training regimes.

What this does not establish

These are empirical relationships over investigated ranges. They do not prove indefinite improvement, factual reliability, or inevitable general intelligence.

Why this date?

The first arXiv submission was 23 January 2020.

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The original work

Scaling Laws for Neural Language Models

Authors: Jared Kaplan and colleagues
Publication: arXiv:2001.08361
Source type: Preprint

Paper and version history

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Cited By

Atlas entries whose reference lists cite this work or the publication version noted below.

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GPT-4 Technical Report (2023)
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LLaMA: Open and Efficient Foundation Language Models (2023)
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Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context (2024)
Listed in the source bibliography. Check the reference list.

Citations

Works in the Atlas cited by this entry.

ImageNet Classification with Deep Convolutional Neural Networks (2012)
Listed in the source bibliography. Check the reference list.
Attention Is All You Need (2017)
Listed in the source bibliography. Check the reference list.
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2018)
Listed in the source bibliography. Check the reference list.
Language Models are Unsupervised Multitask Learners (2019)
Listed in the source bibliography. Check the reference list.

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Reference section 1 (PDF pages 28, 29, 30, 31)

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