What is recorded here?
Measured approximate power-law relationships between language-model cross-entropy loss and model size, dataset size and training compute over the studied ranges, and derived compute-allocation implications.
Source-linked catalogue note. A detailed explanatory guide has not yet been prepared for this entry.
Scientific scope
These are empirical relationships for the architectures, data and loss regimes tested in the 2020 paper, not universal laws of intelligence or guarantees outside the measured ranges.
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People and systems
Jared Kaplan; Sam McCandlish; Tom Henighan; Tom B. Brown; Benjamin Chess; Rewon Child; Scott Gray; Alec Radford; Jeffrey Wu; Dario Amodei
The named authors contributed to this scoped work; individual effort shares and later applications are not assigned by the byline.
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English catalogue narrative
Kaplan and colleagues: scaling laws for neural language models
Measured approximate power-law relationships between language-model cross-entropy loss and model size, dataset size and training compute over the studied ranges, and derived compute-allocation implications.
Scientific scope
These are empirical relationships for the architectures, data and loss regimes tested in the 2020 paper, not universal laws of intelligence or guarantees outside the measured ranges.
Reading status
This is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.
People and systems
Jared Kaplan; Sam McCandlish; Tom Henighan; Tom B. Brown; Benjamin Chess; Rewon Child; Scott Gray; Alec Radford; Jeffrey Wu; Dario Amodei
The named authors contributed to this scoped work; individual effort shares and later applications are not assigned by the byline.