用零样本推理和程序合成，让AI准确解大学级数学题
REAMS: Reasoning Enhanced Algorithm for Maths Solving
- 结合程序合成与零样本学习，减少对大规模数据依赖
- 在MIT/Columbia及MATH数据集上达到90.15%准确率
- 适合研究自动数学求解与AI推理能力的学者
解决大学级数学难题（如麻省理工学院、哥伦比亚大学课程题目及部分MATH数据集任务）仍是人工智能领域的重大挑战。传统方法表现不佳，亟需更先进方案。本文提出一种基于语言的求解方法，利用零样本学习与数学推理，有效解答、解释并生成复杂数学问题的解法。通过引入程序合成，降低对大规模训练数据的依赖，同时显著提升求解准确率。该方法在相关任务上实现90.15%的准确率，远超此前81%的基准水平，树立了自动化数学求解的新标准。研究结果表明，先进AI方法在应对最复杂数学课程与数据集方面具有巨大潜力。
原文摘要 · Abstract (English)
The challenges of solving complex university-level mathematics problems, particularly those from MIT, and Columbia University courses, and selected tasks from the MATH dataset, remain a significant obstacle in the field of artificial intelligence. Conventional methods have consistently fallen short in this domain, highlighting the need for more advanced approaches. In this paper, we introduce a language-based solution that leverages zero-shot learning and mathematical reasoning to effectively solve, explain, and generate solutions for these advanced math problems. By integrating program synthesis, our method reduces reliance on large-scale training data while significantly improving problem-solving accuracy. Our approach achieves an accuracy of 90.15%, representing a substantial improvement over the previous benchmark of 81% and setting a new standard in automated mathematical problem-solving. These findings highlight the significant potential of advanced AI methodologies to address and overcome the challenges presented by some of the most complex mathematical courses and datasets.
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