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...
What happened
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...
Why it matters
The development may change operating conditions or market expectations around AI. Further confirmation and measurable outcomes matter.
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1 reports · 1 original report · 1 independent
- Apple Machine Learning ResearchPrimary source · Supports · EN · 100%Scaling Laws for Mixture Pretraining Under Data Constraints ↗
Claims
- Scaling Laws for Mixture Pretraining Under Data Constraints Observed
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Timeline
- First reported
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