@inproceedings{zhang-etal-2025-autonomous,
title = "Autonomous Data Selection with Zero-shot Generative Classifiers for Mathematical Texts",
author = "Zhang, Yifan and
Luo, Yifan and
Yuan, Yang and
Yao, Andrew C",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-acl.216/",
doi = "10.18653/v1/2025.findings-acl.216",
pages = "4168--4189",
ISBN = "979-8-89176-256-5",
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."
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<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.</abstract>
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%0 Conference Proceedings
%T Autonomous Data Selection with Zero-shot Generative Classifiers for Mathematical Texts
%A Zhang, Yifan
%A Luo, Yifan
%A Yuan, Yang
%A Yao, Andrew C.
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Findings of the Association for Computational Linguistics: ACL 2025
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-256-5
%F zhang-etal-2025-autonomous
%X 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.
%R 10.18653/v1/2025.findings-acl.216
%U https://aclanthology.org/2025.findings-acl.216/
%U https://doi.org/10.18653/v1/2025.findings-acl.216
%P 4168-4189
Markdown (Informal)
[Autonomous Data Selection with Zero-shot Generative Classifiers for Mathematical Texts](https://aclanthology.org/2025.findings-acl.216/) (Zhang et al., Findings 2025)
ACL