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This repository showcases our recent research aimed at improving the accuracy of large language models (LLMs) in mathematical domains. We believe our approach has surpassed previous methods, such as chain-of-thought and graph-of-thought techniques, achieving state-of-the-art performance.
In recent years, numerous prompting methods have been developed to guide large language models (LLMs) in tackling mathematical problems. However, their mathematical performance still falls short of satisfaction espically without fine tuning or zero-shot prompting. As a result, we have devised a novel approach to enhance this performance, which we call "divide and conquer." Unlike traditional applications of divide and conquer, our method proposes utilizing a programming language, such as Python, combined with an interpreter, simulating the way a human uses a calculator.
Our algorithm primarily focuses on mathematical problems, particularly computational challenges rather than proof-based issues. It first assesses whether the problem is a mathematical one and then divides it into subproblems until it can be solved using Python programming. This approach significantly reduces calculation errors, and we believe that our performance, even without fine-tuning the model, has reached state-of-the-art levels.
