Large language models (LLMs) have shown promising performance on various NLP tasks via task prompting. And their performance can be further improved by appending task demonstrations to the head of the prompt. And usually, a better performance can be achieved with more demonstrations. However, asking the users to write the demonstrations can be cumbersome. As a simple yet cost-effective workaround, this paper proposes a novel method called EPA (\textbf{E}asy \textbf{P}rompt \textbf{A}ugmentation)\footnote{While this paper considers augmenting prompts via demonstrations, we name it EPA as the name EDA is already taken by a well-known NLP method \citep{wei-zou-2019-eda}.} that effectively minimizes user efforts in writing demonstrations while improving the model performance at the same time. EPA achieves these goals by automatically augmenting the demonstrations with multiple sources/targets, where each of them paraphrases each other. This is well motivated as augmenting data via paraphrasing effectively improves neural language models. EPA thus employs paraphrasing as an augmentation method for in-context learning. Extensive experiments indicate that EPA effectively improves both NLU and NLG tasks, covering from natural language inference to machine translation in translating tens of languages.\footnote{Code and data will be released upon publication.}

翻译：大语言模型（LLMs）通过任务提示方法在各类自然语言处理任务中展现出优异性能。在提示前添加任务示范（demonstrations）可进一步提升模型表现，通常示范数量越多，效果越佳。然而，要求用户编写示范往往较为繁琐。针对这一难题，本文提出一种简单且经济高效的创新方法——EPA（便捷提示增强，Easy Prompt Augmentation），该方法在提升模型性能的同时，最大限度减少用户编写示范的工作量。EPA通过自动生成来自多源/多目标的示范增强实现上述目标，其中各示范互为改述。这一设计的理论基础在于：基于改述的数据增强能有效改进神经语言模型。因此EPA将改述作为上下文学习中的增强方法。大量实验表明，EPA能显著提升自然语言理解（NLU）与自然语言生成（NLG）任务的表现，覆盖从自然语言推理到涉及数十种语言的机器翻译任务。