In this work, we investigate whether small language models can determine
high-quality subsets of large-scale text datasets that improve the performance
of larger language models. While existing work has shown that pruning based on
the perplexity of a larger model can yield high-quality data, we investigate
whether smaller models can be used for perplexity-based pruning and how pruning
is affected by the domain composition of the data being pruned. We demonstrate
that for multiple dataset compositions, perplexity-based pruning of pretraining
data can \emph{significantly} improve downstream task performance: pruning
based on perplexities computed with a 125 million parameter model improves the
average performance on downstream tasks of a 3 billion parameter model by up to
2.04 and achieves up to a $1.45\times$ reduction in pretraining steps to reach
commensurate baseline performance. Furthermore, we demonstrate that such
perplexity-based data pruning also yields downstream performance gains in the
over-trained and data-constrained regimes.

通过研究发现，对大规模文本数据集进行小型语言模型基于困惑度的修剪，可以显著提高后续任务的性能，并且能够在过度训练和数据受限制的情况下获得下游性能增益。