Selecting high-quality pre-training data is important for creating capable
language models, but existing methods rely on simple heuristics. We introduce
QuRating, a method for selecting pre-training data that captures the abstract
qualities of texts which humans intuitively perceive. In this paper, we
investigate four qualities - writing style, required expertise, facts & trivia,
and educational value. We find that LLMs are able to discern these qualities
and observe that they are better at making pairwise judgments of texts than at
rating the quality of a text directly. We train a QuRater model to learn scalar
ratings from pairwise judgments, and use it to annotate a 260B training corpus
with quality ratings for each of the four criteria. In our experiments, we
select 30B tokens according to the different quality ratings and train
1.3B-parameter language models on the selected data. We find that it is
important to balance quality and diversity, as selecting only the highest-rated
documents leads to poor results. When we sample using quality ratings as logits
over documents, our models achieve lower perplexity and stronger in-context
learning performance than baselines. Beyond data selection, we use the quality
ratings to construct a training curriculum which improves performance without
changing the training dataset. We extensively analyze the quality ratings and
discuss their characteristics, biases, and wider implications.

使用 QuRating 方法选择预训练数据，可以捕捉人们直观感知的文本抽象品质。通过对四个品质进行分析，我们发现 LLMs 在进行文本配对判断方面表现优于直接评价文本质量。使用 QuRater 模型学习从配对判断中学习标量评分，并使用它为 260B 训练语料库的每个标准进行质量评级。根据不同的质量评级选择 30B 令牌，并在选定的数据上训练 13B 参数的语言模型。平衡质量和多样性很重要，仅选择最高评级的文档会导致较差的结果。通过使用质量评级作为逻辑的采样，我们的模型在困惑度和上下文学习性能方面均优于基准模型。除了数据选择外，我们使用质量评级构建了一个训练课程，提高了性能而无需更改训练数据集。我们对质量评级进行了广泛分析，并讨论了其特征、偏见和广泛的影响。