AutoMathText: Autonomous Data Selection with Language Models for
  Mathematical Texts
Authors: name: Yifan Zhang, name: Yifan Luo, name: Yang Yuan, name: Andrew Chi-Chih Yao
Organization: math-ai

To improve language models' proficiency in mathematical reasoning via
continual pretraining, we introduce a novel strategy that leverages base
language models for autonomous data selection. Departing from conventional
supervised fine-tuning or trained classifiers with human-annotated data, our
approach utilizes meta-prompted language models as zero-shot verifiers to
autonomously evaluate and select high-quality mathematical content, and we
release the curated open-source AutoMathText dataset encompassing over 200GB of
data. To demonstrate the efficacy of our method, we continuously pretrained a
7B-parameter Mistral language model on the AutoMathText dataset, achieving
substantial improvements in downstream performance on the MATH dataset with a
token amount reduced by orders of magnitude compared to previous continuous
pretraining works. Our method showcases a 2 times increase in pretraining token
efficiency compared to baselines, underscoring the potential of our approach in
enhancing models' mathematical reasoning capabilities. The AutoMathText dataset
is available at https://huggingface.co/datasets/math-ai/AutoMathText. The code
is available at https://github.com/yifanzhang-pro/AutoMathText.