MetaRuleGPT: Recursive Numerical Reasoning of Language Models Trained with Simple Rules
This addresses the problem of poor numerical reasoning in language models for AI applications, representing an incremental improvement through rule-based learning.
The paper tackles the limitation of large language models in mathematical reasoning by proposing MetaRuleGPT, a Transformer-based architecture that learns and combines rules for numerical calculations and logical operations, achieving accurate results for complex problems.
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.