AutoMathText: Autonomous Data Selection with Language Models for Mathematical Texts

Yifan Zhang, Yifan Luo, Yang Yuan, Andrew Chi-Chih Yao

AutoMathText: Autonomous Data Selection with Language Models for Mathematical Texts: 18 upvotes on Hugging Face Daily Papers, #5 of 16 papers on 2024-02-13. Day-by-day upvote history.

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.

Paper page on Hugging Face · arXiv

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