Let's Reason Formally: Natural-Formal Hybrid Reasoning Enhances LLM's Math Capability

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Paper Summary

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Mixing Math Methods: Hybrid Reasoning Gives LLMs a Boost

This paper proposes a new framework, NL-FL HybridReasoning, that enhances the math capabilities of Large Language Models (LLMs). It integrates natural language (NL) and formal language (FL) reasoning through problem alignment, mixed problem input, and answer extraction techniques, achieving improved accuracy on MATH-500 and AMC benchmarks. The framework also showcases the unique capabilities of FL reasoning by solving problems that are difficult for pure NL models, even with multiple attempts.

Possible Conflicts of Interest

None identified

Identified Weaknesses

Dataset not released

The dataset is not publicly available yet.

Lack of external validation

The paper lacks external validation of the proposed framework.

Limited comparison with other methods

The results, while promising, have not been extensively compared to other state-of-the-art methods.

Rating Explanation

The paper presents a novel and promising approach to improve mathematical reasoning in LLMs. The hybrid framework combines the strengths of both NL and FL reasoning, leading to improved accuracy. While more validation and comparison with other methods is needed, the initial results are encouraging.

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