RevOrder: A Novel Method for Enhanced Arithmetic in Language Models
- URL: http://arxiv.org/abs/2402.03822v2
- Date: Sat, 24 Feb 2024 01:11:14 GMT
- Title: RevOrder: A Novel Method for Enhanced Arithmetic in Language Models
- Authors: Si Shen, Peijun Shen, Danhao Zhu
- Abstract summary: RevOrder reverses 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 $mathcalO(1)$.
- Score: 0.9043578619916238
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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.
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