大型语言模型是对比推理器
Large Language Models are Contrastive Reasoners
AI总结：
本文提出对比提示方法，通过简单提示提升大型语言模型的推理能力，在算术和常识推理任务中优于零样本和少样本方法。
摘要：
提示方法在增强预训练大型语言模型（LLMs）的能力方面起着关键作用。我们探讨了对比提示（CP）如何显著提高大型语言模型进行复杂推理的能力。我们通过在LLMs提供答案前添加
英文摘要：
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
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
