@inproceedings{wang-lu-2023-learning,
    title = "Learning Multi-Step Reasoning by Solving Arithmetic Tasks",
    author = "Wang, Tianduo  and
      Lu, Wei",
    editor = "Rogers, Anna  and
      Boyd-Graber, Jordan  and
      Okazaki, Naoaki",
    booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.acl-short.106",
    doi = "10.18653/v1/2023.acl-short.106",
    pages = "1229--1238",
    abstract = "Mathematical reasoning is regarded as a necessary ability for Language Models (LMs). Recent works demonstrate large LMs{'} impressive performance in solving math problems. The success is attributed to their Chain-of-Thought (CoT) reasoning abilities, i.e., the ability to decompose complex questions into step-by-step reasoning chains, but such ability seems only to emerge from models with abundant parameters. This work investigates how to incorporate relatively small LMs with the capabilities of multi-step reasoning. We propose to inject such abilities by continually pre-training LMs on a synthetic dataset MsAT which is composed of Multi-step Arithmetic Tasks. Our experiments on four math word problem datasets show the effectiveness of the proposed method in enhancing LMs{'} math reasoning abilities.",
}

Mathematical reasoning is regarded as a necessary ability for Language Models (LMs). Recent works demonstrate large LMs{'} impressive performance in solving math problems. The success is attributed to their Chain-of-Thought (CoT) reasoning abilities, i.e., the ability to decompose complex questions into step-by-step reasoning chains, but such ability seems only to emerge from models with abundant parameters. This work investigates how to incorporate relatively small LMs with the capabilities of multi-step reasoning. We propose to inject such abilities by continually pre-training LMs on a synthetic dataset MsAT which is composed of Multi-step Arithmetic Tasks. Our experiments on four math word problem datasets show the effectiveness of the proposed method in enhancing LMs{'} math reasoning abilities.
Wang, Ti uo Lu, Wei
Learning Multi-Step Reasoning by Solving Arithmetic Tasks