Autonomous Data Selection with Zero-shot Generative Classifiers for Mathematical Texts

• URL: http://arxiv.org/abs/2402.07625v4
• Date: Tue, 18 Feb 2025 01:02:54 GMT
• Title: Autonomous Data Selection with Zero-shot Generative Classifiers for Mathematical Texts
• Authors: Yifan Zhang, Yifan Luo, Yang Yuan, Andrew Chi-Chih Yao,
• Abstract summary: We present Autonomous Data Selection (AutoDS), a method that automatically curates high-quality mathematical texts. Unlike prior approaches that require human annotations or training a dedicated data filter, AutoDS relies solely on a model's logits. We release our curated AutoMathText dataset to facilitate future research in automated domain-specific data curation.
• Score: 13.789739307267952
• License:
• Abstract: We present Autonomous Data Selection (AutoDS), a method that leverages base language models themselves as zero-shot "generative classifiers" to automatically curate high-quality mathematical texts. Unlike prior approaches that require human annotations or training a dedicated data filter, AutoDS relies solely on a model's logits to determine whether a given passage is mathematically informative and educational. By integrating AutoDS into a continual pretraining pipeline, we substantially boost downstream performance on challenging math benchmarks (MATH, GSM8K, and BBH) while using far fewer tokens than previous methods. Empirically, our approach achieves roughly a twofold improvement in pretraining token efficiency over strong baselines, underscoring the potential of self-directed data selection in enhancing mathematical reasoning. We release our curated AutoMathText dataset to facilitate future research in automated domain-specific data curation. The AutoMathText dataset is available at https://huggingface.co/datasets/math-ai/AutoMathText. The code is available at https://github.com/yifanzhang-pro/AutoMathText.

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