Language models are achieving impressive performance on various tasks by aggressively adopting inference-time prompting techniques, such as zero-shot and few-shot prompting. In this work, we introduce EchoPrompt, a simple yet effective approach that prompts the model to rephrase its queries before answering them. EchoPrompt is adapted for both zero-shot and few-shot in-context learning with standard and chain-of-thought prompting. Experimental results show that EchoPrompt yields substantial improvements across all these settings for four families of causal language models. These improvements are observed across various numerical reasoning (e.g. GSM8K, SVAMP), reading comprehension (e.g. DROP), and logical reasoning (e.g. Coin Flipping) tasks. On average, EchoPrompt improves the Zero-shot-CoT performance of code-davinci-002 by 5% in numerical tasks and 13% in reading comprehension tasks. We investigate the factors contributing to EchoPrompt's effectiveness through ablation studies, which reveal that both the original query and the model-generated rephrased version are instrumental in its performance gains. Our empirical results indicate that EchoPrompt is an effective technique that enhances in-context learning performance. We recommend incorporating EchoPrompt into various baseline prompting strategies to achieve performance boosts.

翻译：摘要：语言模型通过积极采用推理时提示技术（如零样本和少样本提示），在各种任务上取得了显著性能。本文提出EchoPrompt，一种简单而有效的方法，引导模型在回答问题前重述其查询。EchoPrompt适用于标准提示和链式思维提示下的零样本与少样本上下文学习。实验结果表明，EchoPrompt在四类因果语言模型的所有设置中均带来显著提升。这些改进体现在数值推理（如GSM8K、SVAMP）、阅读理解（如DROP）和逻辑推理（如抛硬币）等多种任务中。平均而言，EchoPrompt将code-davinci-002的零样本链式思维性能在数值任务上提升5%，在阅读理解任务上提升13%。通过消融实验，我们探究了EchoPrompt有效性的成因，发现原始查询和模型生成的重述版本均对其性能提升起关键作用。实证结果表明，EchoPrompt是一种有效增强上下文学习性能的技术。我们建议将EchoPrompt集成到各类基线提示策略中，以实现性能提升。