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    <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 “less is more” 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.</abstract>
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%0 Conference Proceedings
%T ControlMath: Controllable Data Generation Promotes Math Generalist Models
%A Chen, Nuo
%A Wu, Ning
%A Chang, Jianhui
%A Shou, Linjun
%A Li, Jia
%Y Al-Onaizan, Yaser
%Y Bansal, Mohit
%Y Chen, Yun-Nung
%S Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F chen-etal-2024-controlmath
%X 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 “less is more” 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.
%U https://aclanthology.org/2024.emnlp-main.680
%P 12201-12217

Markdown (Informal)

[ControlMath: Controllable Data Generation Promotes Math Generalist Models](https://aclanthology.org/2024.emnlp-main.680) (Chen et al., EMNLP 2024)

• ControlMath: Controllable Data Generation Promotes Math Generalist Models (Chen et al., EMNLP 2024)

ACL

• Nuo Chen, Ning Wu, Jianhui Chang, Linjun Shou, and Jia Li. 2024. ControlMath: Controllable Data Generation Promotes Math Generalist Models. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 12201–12217, Miami, Florida, USA. Association for Computational Linguistics.