Improving Pretraining Data Using Perplexity Correlations

• URL: http://arxiv.org/abs/2409.05816v1
• Date: Mon, 9 Sep 2024 17:23:29 GMT
• Title: Improving Pretraining Data Using Perplexity Correlations
• Authors: Tristan Thrush, Christopher Potts, Tatsunori Hashimoto,
• Abstract summary: We build a new statistical framework for data selection centered around estimates of perplexity-benchmark correlations. In controlled pretraining experiments at the 160M parameter scale on 8 benchmarks, our approach outperforms DSIR on every benchmark.
• Score: 56.41097718862742
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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