This paper presents RevOrder, a novel technique aimed at improving arithmetic operations in large language models (LLMs) by reversing the output digits in addition, subtraction, and n-digit by 1-digit (nD by 1D) multiplication tasks. Our method significantly reduces the Count of Sequential Intermediate Digits (CSID) to $\mathcal{O}(1)$, a new metric we introduce to assess equation complexity. Through comprehensive testing, RevOrder not only achieves perfect accuracy in basic arithmetic operations but also substantially boosts LLM performance in division tasks, particularly with large numbers where traditional models struggle. Implementation of RevOrder is cost-effective for both training and inference phases. Moreover, applying RevOrder to fine-tune the LLaMA2-7B model on the GSM8K math task results in a considerable improvement, reducing equation calculation errors by 46% and increasing overall scores from 41.6 to 44.4.

RevOrder是一种新颖的技术，通过颠倒输出数字来改善大型语言模型中的算术运算，在加法、减法和n位乘1位（nD乘1D）乘法任务中实现了关键的CSID指标显著降低。RevOrder不仅在基本算术运算中达到了完美的准确性，而且还显著提升了LLM在除法任务中的性能，特别是在传统模型难以应对的大数情况下。将RevOrder应用于GSM8K数学任务的LLMaMA2-7B模型的微调中，引发了46%的计算错误减少和总分数从41.6提高到44.4的显著改进。

RevOrder：一种增强语言模型中算术运算的新方法