Deliberate then Generate: Enhanced Prompting Framework for Text Generation
This addresses the challenge of enhancing prompt designs for text generation tasks like summarization and translation, offering a simple yet effective technique with broad applicability, though it appears incremental as it builds on existing prompting methods.
The paper tackles the problem of improving text generation with large language models by proposing a Deliberate then Generate (DTG) prompting framework, which includes error detection instructions and candidates, and shows that it consistently outperforms existing methods and achieves state-of-the-art performance on over 20 datasets across 7 tasks.
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.