Large Language Models are Contrastive Reasoners

TL;DR

This paper introduces contrastive prompting, a simple yet effective method that significantly enhances large language models' reasoning abilities across various tasks without additional training or examples.

Contribution

The paper demonstrates that zero-shot contrastive prompting markedly improves LLM reasoning performance and can be combined with existing methods for further gains.

Findings

Zero-shot contrastive prompting boosts accuracy on arithmetic and reasoning tasks.

Contrastive prompting outperforms zero-shot and few-shot chain-of-thought methods.

The approach is compatible with existing prompting techniques.

Abstract

Prompting methods play a crucial role in enhancing the capabilities of pre-trained large language models (LLMs). We explore how contrastive prompting (CP) significantly improves the ability of large language models to perform complex reasoning. We demonstrate that LLMs are decent contrastive reasoners by simply adding "Let's give a correct and a wrong answer." before LLMs provide answers. Experiments on various large language models show that zero-shot contrastive prompting improves the performance of standard zero-shot prompting on a range of arithmetic, commonsense, and symbolic reasoning tasks without any hand-crafted few-shot examples, such as increasing the accuracy on GSM8K from 35.9% to 88.8% and AQUA-RAT from 41.3% to 62.2% with the state-of-the-art GPT-4 model. Our method not only surpasses zero-shot CoT and few-shot CoT in most arithmetic and commonsense reasoning tasks but also can seamlessly integrate with existing prompting methods, resulting in improved or comparable results when compared to state-of-the-art methods. Our code is available at https://github.com/yao8839836/cp