Deliberate then Generate: Enhanced Prompting Framework for Text
  Generation
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.
6478045bdbc2a416f8ba9178
Bei Li
6478045bdbc2a416f8ba9179
Rui Wang
6478045bdbc2a416f8ba917a
Junliang Guo
6478045bdbc2a416f8ba917b
Kaitao Song
admin_assigned
2023-06-01T08:39:16.223Z
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Kaitao Song
user
KaitaoSong
6478045bdbc2a416f8ba917c
Xu Tan
admin_assigned
2023-06-01T08:39:40.573Z
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/avatars/a2f28940236ae625ed3810ad62e343ff.svg
Xu Tan
user
xutan
6478045bdbc2a416f8ba917d
Hany Hassan
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Arul Menezes
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Tong Xiao
6478045bdbc2a416f8ba9180
Jiang Bian
claimed_verified
2025-06-19T09:12:41.257Z
683843c8a9bdf98f92657042
/avatars/4983876f615fa830cfc8f2cb5a00fa08.svg
Jiang Bian
user
jiangbian22
6478045bdbc2a416f8ba9181
JingBo Zhu
Deliberate then Generate: Enhanced Prompting Framework for Text
  Generation
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.
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.
Large language models (LLMs) Deliberate then Generate (DTG) error detection instructions candidate suggestions text generation tasks summarization translation dialogue