Year: 2023
Type: conference-paper
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.1
Authors: Tianduo Wang, Wei Lu
Institution: Singapore University of Technology and Design
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4385571387