Improving Pretraining Data Using Perplexity Correlations

ICLR 2025 Conference Submission8332 Authors

Keywords: pretraining data; data selection; natural language processing; statistics; large language models

TL;DR: We present new lightweight statistical methods for pretraining data selection. Our paradigm requires no LLM training, matches the best known existing method in our experiments, and has some nice theory.

Abstract: Quality pretraining data is often seen as the key to high-performance language models. However, progress in understanding pretraining data has been slow due to the costly pretraining runs required for data selection experiments. We present a framework that avoids these costs and selects high-quality pretraining data without any LLM training of our own. Our work is based on a simple observation: LLM losses on many pretraining texts are correlated with downstream benchmark performance, and selecting high-correlation documents is an effective pretraining data selection method. We build a new statistical framework for data selection centered around estimates of perplexity-benchmark correlations and perform data selection using a sample of 90 LLMs taken from the Open LLM Leaderboard on texts from tens of thousands of web domains. In controlled pretraining experiments at the 160M parameter scale on 8 benchmarks, our approach outperforms DSIR on every benchmark, while matching the best data selector found in DataComp-LM, a hand-engineered bigram classifier.

Primary Area: foundation or frontier models, including LLMs

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Submission Number: 8332