Rephrasing the Web: A Recipe for Compute and Data-Efficient Language
  Modeling
Pratyush Maini Skyler Seto He Bai David Grangier Yizhe Zhang Navdeep Jaitly
Large language models are trained on massive scrapes of the web, which are
often unstructured, noisy, and poorly phrased. Current scaling laws show that
learning from such data requires an abundance of both compute and data, which
grows with the size of the model being trained. This is infeasible both because
of the large compute costs and duration associated with pre-training, and the
impending scarcity of high-quality data on the web. In this work, we propose
Web Rephrase Augmented Pre-training ($\textbf{WRAP}$) that uses an
off-the-shelf instruction-tuned model prompted to paraphrase documents on the
web in specific styles such as "like Wikipedia" or in "question-answer format"
to jointly pre-train LLMs on real and synthetic rephrases. First, we show that
using WRAP on the C4 dataset, which is naturally noisy, speeds up pre-training
by $\sim3x$. At the same pre-training compute budget, it improves perplexity by
more than 10% on average across different subsets of the Pile, and improves
zero-shot question answer accuracy across 13 tasks by more than 2%. Second, we
investigate the impact of the re-phrasing style on the performance of the
model, offering insights into how the composition of the training data can
impact the performance of LLMs in OOD settings. Our gains are attributed to the
fact that re-phrased synthetic data has higher utility than just real data
because it (i) incorporates style diversity that closely reflects downstream
evaluation style, and (ii) has higher 'quality' than web-scraped data.