  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.
