Predictive Data Selection: The Data That Predicts Is the Data That
  Teaches
Authors: name: Kashun Shum, name: Yuzhen Huang, name: Hongjian Zou, name: Ding Qi, name: Yixuan Liao, name: Xiaoxin Chen, name: Qian Liu, name: Junxian He

Language model pretraining involves training on extensive corpora, where data
quality plays a pivotal role. In this work, we aim to directly estimate the
contribution of data during pretraining and select pretraining data in an
efficient manner. Specifically, we draw inspiration from recent findings
showing that compression efficiency (i.e., the normalized loss) of diverse
models on certain text correlates strongly with their downstream performance,
when the text domain aligns with the downstream benchmark (Huang et al., 2024).
Building on this observation, we hypothesize that data on which model losses
are predictive of downstream abilities also contribute effectively to learning.
To leverage this insight, we introduce data selection based on data's
Predictive strength (Preselect), a lightweight and efficient data selection
method that requires training and deploying only a fastText-based scorer.
Through comprehensive experiments with 1B and 3B parameter models, we
demonstrate that models trained on 30B tokens selected with PreSelect surpasses
the performance of a vanilla baseline trained on 300B tokens, achieving a 10x
reduction in compute requirements. Furthermore, PreSelect significantly
outperforms other competitive data selection baselines, such as DCLM and
FineWeb-Edu on a scale of 3B models trained on 100B tokens. We open-source our
trained data selection scorer along with the curated datasets at
https://github.com/hkust-nlp/PreSelect.