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Large language models are commonly trained on a mixture of filtered web dataand curated high-quality corpora, such as social media conversations, books, ortechnical papers. This curation process is believed to be necessary to produceperformant models with broad zero-shot generalization abilities. However, aslarger models requiring pretraining on trillions of tokens are considered, itis unclear how scalable is curation and whether we will run out of uniquehigh-quality data soon. At variance with previous beliefs, we show thatproperly filtered and deduplicated web data alone can lead to powerful models;even significantly outperforming models from the state-of-the-art trained onThe Pile. Despite extensive filtering, the high-quality data we extract fromthe web is still plentiful, and we are able to obtain five trillion tokens fromCommonCrawl. We publicly release an extract of 600 billion tokens from ourRefinedWeb dataset, and 1.3/7.5B parameters language models trained on it.

The RefinedWeb Dataset for Falcon LLM: Outperforming Curated Corpora with Web Data, and Web Data Only