Papers by Barret Zoph

1 papers
A Pretrainer’s Guide to Training Data: Measuring the Effects of Data Age, Domain Coverage, Quality, & Toxicity (2024.naacl-long)

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Challenge: a large number of pretraining data design practices are under-documented, authors say . authors: strong performance of modern language models depends on selfsupervised pretraining .
Approach: They propose to pretrain models on data curated at different collection times . they find temporal shift between evaluation data and pretraining data leads to performance degradation .
Outcome: The results validate, quantify, and expose many undocumented intuitions about text pretraining . authors say this practice has outperformed other models in the field .

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