Language models can be efficiently trained using unlabeled data, allowing for the creation of large datasets from curated and web crawl sources, which improves model performance.
Curated data, derived from quality sources like Wikipedia and news, is often preferred for early LLMs, but web crawl data is becoming increasingly significant for larger models.
Web crawl data provides a vast array of text types, enhancing the diversity and scale of training data, which is essential as the size of models advances.
CommonCrawl has been pivotal in gathering extensive web data, linking the growing size of language models to the potential of utilizing diverse Internet content.
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