Rephrasing the Web: A Recipe for Compute and Data-Efficient Language Modeling

This paper has been accepted at the Data Problems for Foundation Models workshop at ICLR 2024.
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 proposeWebRephrase Augmented Pre-training…