@inproceedings{
wang2023learning,
title={Learning Multi-Step Reasoning by Solving Arithmetic Tasks},
author={Tianduo Wang and Wei Lu},
booktitle={The 3rd Workshop on Mathematical Reasoning and AI at NeurIPS'23},
year={2023},
url={https://openreview.net/forum?id=Ni1KnzY5KN}
}
Mathematical reasoning is regarded as a neces- sary 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.
Learning Multi-Step Reasoning by Solving Arithmetic Tasks
Tianduo Wang Wei Lu