title: Deliberate then Generate: Enhanced Prompting Framework for Text
    Generation
summary: 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.
paper_authors._id: 6478045bdbc2a416f8ba9178
paper_authors.name: Bei Li
paper_authors._id: 6478045bdbc2a416f8ba9179
paper_authors.name: Rui Wang
paper_authors._id: 6478045bdbc2a416f8ba917a
paper_authors.name: Junliang Guo
paper_authors._id: 6478045bdbc2a416f8ba917b
paper_authors.name: Kaitao Song
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-06-01T08:39:16.223Z
paper_authors._id: 5fc0b2b61160c47d1d438568
paper_authors.avatarUrl: /avatars/b355912b0ec683e73f21c8d36620e146.svg
paper_authors.fullname: Kaitao Song
paper_authors.type: user
paper_authors.user: KaitaoSong
paper_authors._id: 6478045bdbc2a416f8ba917c
paper_authors.name: Xu Tan
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-06-01T08:39:40.573Z
paper_authors._id: 5f1040b6e9d71719e3be71d2
paper_authors.avatarUrl: /avatars/a2f28940236ae625ed3810ad62e343ff.svg
paper_authors.fullname: Xu Tan
paper_authors.type: user
paper_authors.user: xutan
paper_authors._id: 6478045bdbc2a416f8ba917d
paper_authors.name: Hany Hassan
paper_authors._id: 6478045bdbc2a416f8ba917e
paper_authors.name: Arul Menezes
paper_authors._id: 6478045bdbc2a416f8ba917f
paper_authors.name: Tong Xiao
paper_authors._id: 6478045bdbc2a416f8ba9180
paper_authors.name: Jiang Bian
paper_authors._id: 6478045bdbc2a416f8ba9181
paper_authors.name: JingBo Zhu
paper_title: Deliberate then Generate: Enhanced Prompting Framework for Text
    Generation
paper_summary: 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.
paper_ai_summary: A Deliberate then Generate (DTG) prompting framework enhances large language models by incorporating error detection and candidate suggestions to achieve superior performance across multiple text generation tasks.
paper_ai_keywords: Large language models (LLMs) Deliberate then Generate (DTG) error detection instructions candidate suggestions text generation tasks summarization translation dialogue