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SQLPrompt: In-Context Text-to-SQL with Minimal Labeled Data

Ruoxi SunSercan Ö. ArikRajarishi SinhaHootan NakhostHanjun DaiPengcheng YinTomas Pfister • @arXiv • 06 November 2023

TLDR: It is shown 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.

Citations: 6
Abstract: 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.

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