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Deliberate then Generate: Enhanced Prompting Framework for Text Generation

arXiv 2305.19835 ↗First posted 31 May 2023cs.CL cs.AIcited by 13

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Abstract (arXiv)

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.

Key facts. Cited by 13 papers in the map (data through 7 Oct 2026). Builds on 42 of its 51 references that are in the map. First posted on arXiv 31 May 2023.

Builds on

42 of 51 references.

References · 42

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30 more
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Citing papers per year: 2023: 1; 2024: 8; 2025: 4.

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  1. 2023EvoPrompt: Connecting LLMs with Evolutionary Algorithms Yields Powerful Prompt Optimizerscited by 189
  2. 2025A Systematic Survey of Automatic Prompt Optimization Techniquescited by 61
  3. 2024Efficient Prompting Methods for Large Language Models: A Surveycited by 46
  4. 2024TasTe: Teaching Large Language Models to Translate through Self-Reflectioncited by 27
  5. 2024Improving Diversity of Commonsense Generation by Large Language Models via In-Context Learningcited by 19
  6. 2024Video In-context Learning: Autoregressive Transformers are Zero-Shot Video Imitatorscited by 9
  7. 2024Rethinking Semantic Parsing for Large Language Models: Enhancing LLM Performance with Semantic Hintscited by 5
  8. 2025Generative Product Recommendations for Implicit Superlative Queriescited by 5
  9. 2024AutoFeedback: An LLM-based Framework for Efficient and Accurate API Request Generationcited by 3
  10. 2024Refining Answer Distributions for Improved Large Language Model Reasoningcited by 3
  11. 2025A Toolbox for Improving Evolutionary Prompt Searchcited by 3
  12. 2025Which Words Matter Most in Zero-Shot Prompts?cited by 2
1 more
  1. 2024Improving Embedding Accuracy for Document Retrieval Using Entity Relationship Maps and Model-Aware Contrastive Samplingcited by 0