@inproceedings{chen-etal-2024-controlmath,
    title = "{C}ontrol{M}ath: Controllable Data Generation Promotes Math Generalist Models",
    author = "Chen, Nuo  and
      Wu, Ning  and
      Chang, Jianhui  and
      Shou, Linjun  and
      Li, Jia",
    editor = "Al-Onaizan, Yaser  and
      Bansal, Mohit  and
      Chen, Yun-Nung",
    booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2024",
    address = "Miami, Florida, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2024.emnlp-main.680/",
    doi = "10.18653/v1/2024.emnlp-main.680",
    pages = "12201--12217",
    abstract = "Utilizing large language models (LLMs) for data augmentation has yielded encouraging results in mathematical reasoning. However, these approaches face constraints in problem diversity, potentially restricting them to in-domain/distribution data generation. To this end, we propose **ControlMath**, an iterative method involving an equation-generator module and two LLM-based agents. The module creates diverse equations, which the Problem-Crafter agent then transforms into math word problems. The Reverse-Agent filters and selects high-quality data, adhering to the {\textquotedblleft}less is more{\textquotedblright} principle. This approach enables the generation of diverse math problems, not limited to specific domains or distributions. As a result, we collect ControlMathQA, which involves 190k math word problems. Extensive results prove that combining our dataset with in-domain datasets like GSM8K can help improve the model`s mathematical ability to generalize, leading to improved performance both within and beyond specific domains."
}