深思熟慮後生成：用於文本生成的增強提示框架

摘要

大型語言模型（LLMs）在各種自然語言生成任務中展現出卓越的成功，適當的提示設計對其影響深遠。現有的提示方法通常僅限於提供正確信息，但在本文中，我們鼓勵模型通過提出一個新穎的「先思考後生成」（DTG）提示框架來深入思考，該框架包括錯誤檢測指示和可能包含錯誤的候選項。DTG是一種簡單而有效的技術，可以應用於各種文本生成任務，並且只需進行最少的修改。我們在包括摘要、翻譯、對話等在內的7個文本生成任務上對20多個數據集進行了廣泛實驗。我們展示了DTG始終優於現有提示方法，在多個文本生成任務上實現了最先進的性能。我們還進行了深入分析，揭示了DTG的潛在機制，這可能激發對LLMs提示的未來研究。

English

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.

深思熟慮後生成：用於文本生成的增強提示框架

Deliberate then Generate: Enhanced Prompting Framework for Text Generation

摘要

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