Text-to-SQL aims to automate the process of generating SQL queries on a
database from natural language text. In this work, we propose "SQLPrompt",
tailored to improve the few-shot prompting capabilities of Text-to-SQL for
Large Language Models (LLMs). Our methods include innovative prompt design,
execution-based consistency decoding strategy which selects the SQL with the
most consistent execution outcome among other SQL proposals, and a method that
aims to improve performance by diversifying the SQL proposals during
consistency selection with different prompt designs ("MixPrompt") and
foundation models ("MixLLMs"). We show that \emph{SQLPrompt} outperforms
previous approaches for in-context learning with few labeled data by a large
margin, closing the gap with finetuning state-of-the-art with thousands of
labeled data.

提出了 “SQLPrompt” 方法，用于改善大型语言模型 (Text-to-SQL) 在少样本预测上的效果，包括创新的提示设计、基于执行结果一致性的解码策略、以及通过不同提示设计和基础模型的混合来提高性能，实验证明 “SQLPrompt” 在有限标注数据的情境学习中显著优于之前的方法，缩小了与使用数千标注数据进行微调的最先进方法的差距。