Large language models (LLMs) with in-context learning have demonstrated remarkable capability in the text-to-SQL task. Previous research has prompted LLMs with various demonstration-retrieval strategies and intermediate reasoning steps to enhance the performance of LLMs. However, those works often employ varied strategies when constructing the prompt text for text-to-SQL inputs, such as databases and demonstration examples. This leads to a lack of comparability in both the prompt constructions and their primary contributions. Furthermore, selecting an effective prompt construction has emerged as a persistent problem for future research. To address this limitation, we comprehensively investigate the impact of prompt constructions across various settings and provide insights for future work.

翻译：大语言模型（LLMs）结合上下文学习已在文本到SQL任务中展现出卓越能力。先前研究采用多种演示检索策略与中间推理步骤来增强大语言模型的性能。然而，这些工作在构建文本到SQL输入的提示文本（如数据库和演示示例）时往往采用不同策略，导致提示构建方式及其主要贡献缺乏可比性。此外，如何选择有效的提示构建方式已成为未来研究的持续难题。为解决这一局限，我们全面研究了不同场景下提示构建方式的影响，并为未来工作提供了见解。