MathLearner: A Large Language Model Agent Framework for Learning to Solve Mathematical Problems

TL;DR

MathLearner is a novel AI framework that enhances mathematical reasoning in large language models by emulating human learning, significantly improving accuracy and problem-solving capabilities in mathematical tasks.

Contribution

The paper introduces MathLearner, an agent framework based on inductive reasoning that improves LLMs' mathematical reasoning by emulating human learning and utilizing efficient retrieval methods.

Findings

Global accuracy improved by 20.96% over baseline

Solves 17.54% more problems than baseline

Enhances external knowledge utilization through retrieval

Abstract

With the development of artificial intelligence (AI), large language models (LLM) are widely used in many fields. However, the reasoning ability of LLM is still very limited when it comes to mathematical reasoning. Mathematics plays an important role in all aspects of human society and is a technical guarantee in the fields of healthcare, transport and aerospace, for this reason, the development of AI big language models in the field of mathematics has great potential significance. To improve the mathematical reasoning ability of large language models, we proposed an agent framework for learning to solve mathematical problems based on inductive reasoning. By emulating the human learning process of generalization of learned information and effective application of previous knowledge in new reasoning tasks, this framework has great performance in the mathematical reasoning process. It improves global accuracy over the baseline method (chain-of-thought) by 20.96% and solves 17.54% of the mathematical problems that the baseline cannot solve. Benefiting from the efficient RETRIEVAL method, our model improves the ability of large language models to efficiently use external knowledge, i.e., the mathematical computation of the model can be based on written procedures. In education, our model can be used as a personalised learning aid, thus reducing the inequality of educational resources.