EPA: Easy Prompt Augmentation on Large Language Models via Multiple
  Sources and Multiple Targets
Hongyuan Lu Wai Lam
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.}