Deliberate then Generate: Enhanced Prompting Framework for Text
  Generation
Bei Li Rui Wang Junliang Guo Kaitao Song Xu Tan Hany Hassan Arul Menezes Tong Xiao Jiang Bian JingBo Zhu
Large language models (LLMs) have shown remarkable success across a wide
range of natural language generation tasks, where proper prompt designs make
great impacts. While existing prompting methods are normally restricted to
providing correct information, in this paper, we encourage the model to
deliberate by proposing a novel Deliberate then Generate (DTG) prompting
framework, which consists of error detection instructions and candidates that
may contain errors. DTG is a simple yet effective technique that can be applied
to various text generation tasks with minimal modifications. We conduct
extensive experiments on 20+ datasets across 7 text generation tasks, including
summarization, translation, dialogue, and more. We show that DTG consistently
outperforms existing prompting methods and achieves state-of-the-art
performance on multiple text generation tasks. We also provide in-depth
analyses to reveal the underlying mechanisms of DTG, which may inspire future
research on prompting for LLMs.