EPA：基于多源和多目标的大型语言模型简易提示增强方法
EPA: Easy Prompt Augmentation on Large Language Models via Multiple Sources and Multiple Targets
AI总结：
本文提出EPA方法，通过多源多目标释义自动增强提示示例，减少用户编写负担的同时提升LLMs在NLU和NLG任务上的性能。
摘要：
大型语言模型（LLMs）通过任务提示在各种NLP任务中展现出了有前景的性能。通过在提示头部添加任务示例，其性能可以进一步提升。通常，使用更多示例可以获得更好的性能。然而，要求用户编写示例可能很繁琐。作为一种简单且高性价比的替代方案，本文提出了一种名为EPA（Easy Prompt Augmentation，简易提示增强）的新方法，该方法有效减少了用户编写示例的工作量，同时提升了模型性能。EPA通过使用多源/多目标自动增强示例来实现这些目标，其中每个源/目标都相互释义。这一方法的动机在于，通过释义增强数据可以有效改善神经语言模型。因此，EPA将释义作为上下文学习的一种增强方法。大量实验表明，EPA有效改善了NLU和NLG任务，涵盖从自然语言推理到翻译数十种语言的机器翻译。
英文摘要：
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.}
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。
