Scaling Laws for Mixture Pretraining Under Data Constraints
As language models scale, the amount of data they require grows – yet many target data sources, such as low-resource languages or specialized domains, are inherently limited in size. A common strategy is to mix this scarce but valuable target data with abundant generic data, which presents a fundamental trade-off: too little target data in the mixture underexposes the model to the target domain, while too much...
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Qué ocurrió
As language models scale, the amount of data they require grows – yet many target data sources, such as low-resource languages or specialized domains, are inherently limited in size. A common strategy is to mix this scarce but valuable target data with abundant generic data, which presents a fundamental trade-off: too little target data in the mixture underexposes the model to the target domain, while too much...
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- Apple Machine Learning ResearchFuente primaria · Respalda · EN · 100%Scaling Laws for Mixture Pretraining Under Data Constraints ↗
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- Scaling Laws for Mixture Pretraining Under Data Constraints Observado
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