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.

翻译：大型语言模型在广泛的自然语言生成任务中展现出显著成功，而恰当的提示设计对其影响巨大。尽管现有提示方法通常局限于提供正确信息，本文通过提出一种新颖的"先思后行"提示框架，鼓励模型进行深思熟虑。该框架包含错误检测指令以及可能包含错误候选项。DTG是一种简单而有效的技术，可经过最小修改适用于各种文本生成任务。我们在涵盖摘要、翻译、对话等7类文本生成任务的20多个数据集上进行了广泛实验，结果表明DTG持续优于现有提示方法，并在多个文本生成任务中达到最先进性能。我们还通过深入分析揭示了DTG的底层机制，这或将为未来关于大型语言模型提示的研究提供启示。