MetaRuleGPT: Recursive Numerical Reasoning of Language Models Trained with Simple Rules

TL;DR

MetaRuleGPT is a Transformer-based model that learns and combines simple rules to perform accurate numerical reasoning and complex logical operations, improving upon traditional language models' mathematical capabilities.

Contribution

It introduces a novel rule-based pre-training approach for language models, enhancing their ability to perform complex mathematical reasoning and logical operations.

Findings

MetaRuleGPT effectively mimics human rule-following in math problems.

It can break down complex problems into simpler rules.

The model derives accurate results for challenging mathematical tasks.

Abstract

Recent studies have highlighted the limitations of large language models in mathematical reasoning, particularly their inability to capture the underlying logic. Inspired by meta-learning, we propose that models should acquire not only task-specific knowledge but also transferable problem-solving skills. We introduce MetaRuleGPT, a novel Transformer-based architecture that performs precise numerical calculations and complex logical operations by learning and combining different rules. In contrast with traditional training sets, which are heavily composed of massive raw instance data, MetaRuleGPT is pre-trained on much less abstract datasets containing basic, compound, and iterative rules for mathematical reasoning. Extensive experimental results demonstrate MetaRuleGPT can mimic human's rule-following capabilities, break down complexity, and iteratively derive accurate results for complex mathematical problems. These findings prove the potential of rule learning to enhance the numerical reasoning abilities of language models.