Large language models (LLMs) recently exhibited remarkable reasoning
capabilities on solving math problems. To further improve this capability, this
work proposes Learning from Mistakes (LeMa), akin to human learning processes.
Consider a human student who failed to solve a math problem, he will learn from
what mistake he has made and how to correct it. Mimicking this error-driven
learning process, LeMa fine-tunes LLMs on mistake-correction data pairs
generated by GPT-4. Specifically, we first collect inaccurate reasoning paths
from various LLMs and then employ GPT-4 as a "corrector" to (1) identify the
mistake step, (2) explain the reason for the mistake, and (3) correct the
mistake and generate the final answer. Experimental results demonstrate the
effectiveness of LeMa: across five backbone LLMs and two mathematical reasoning
tasks, LeMa consistently improves the performance compared with fine-tuning on
CoT data alone. Impressively, LeMa can also benefit specialized LLMs such as
WizardMath and MetaMath, achieving 85.4% pass@1 accuracy on GSM8K and 27.1% on
MATH. This surpasses the SOTA performance achieved by non-execution open-source
models on these challenging tasks. Our code, data and models will be publicly
available at this https URL

利用 Learning from Mistakes（LeMa）方法，本研究利用大型语言模型（LLMs）对数学问题的解决能力进行改进，仿照人类学习过程中的错误驱动学习机制，通过与 GPT-4 生成的错误更正数据对进行微调，实验证明 LeMa 提升了性能，在 5 个 LLMs 和 2 个数学推理任务中持续改善性能，并在特定 LLMs（如 WizardMath 和 MetaMath）上表现出色，在具有挑战性的任务上超越了开源模型的性能。