Learning Multi-Step Reasoning by Solving Arithmetic Tasks

• URL: http://arxiv.org/abs/2306.01707v3
• Date: Wed, 7 Jun 2023 03:45:15 GMT
• Title: Learning Multi-Step Reasoning by Solving Arithmetic Tasks
• Authors: Tianduo Wang and Wei Lu
• Abstract summary: This work investigates how to incorporate relatively small Language Models with the capabilities of multi-step reasoning. We propose to inject such abilities by continually pre-training LMs on a synthetic dataset MsAT. Our experiments on four math word problem datasets show the effectiveness of the proposed method.
• Score: 6.398022050054328
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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