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

ACL ARR 2025 February Submission3007 Authors

15 Feb 2025 (modified: 09 May 2025)ACL ARR 2025 February SubmissionEveryoneRevisionsBibTeXCC BY 4.0
Abstract:

We present Autonomous Data Selection (AutoDS), a method that leverages base language models 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 will release our curated dataset to facilitate future research in automated domain-specific data curation.

Paper Type: Long
Research Area: Resources and Evaluation
Research Area Keywords: automatic evaluation of datasets,automatic creation and evaluation of language resources,language resources,NLP datasets
Contribution Types: Data resources, Data analysis
Languages Studied: English
Submission Number: 3007
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