Let's Reason Formally: Natural-Formal Hybrid Reasoning Enhances LLM's Math Capability
Overview
Paper Summary
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.
Explain Like I'm Five
This paper describes a new way to help computers solve math problems better by combining two different approaches: natural language reasoning and formal language reasoning.
Possible Conflicts of Interest
None identified
Identified Limitations
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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