SQLPrompt: In-Context Text-to-SQL with Minimal Labeled Data
- URL: http://arxiv.org/abs/2311.02883v1
- Date: Mon, 6 Nov 2023 05:24:06 GMT
- Title: SQLPrompt: In-Context Text-to-SQL with Minimal Labeled Data
- Authors: Ruoxi Sun, Sercan \"O. Arik, Rajarishi Sinha, Hootan Nakhost, Hanjun
Dai, Pengcheng Yin, Tomas Pfister
- Abstract summary: "Prompt" is designed to improve the few-shot prompting capabilities of Text-to-LLMs.
"Prompt" outperforms previous approaches for in-context learning with few labeled data by a large margin.
We show that emphPrompt outperforms previous approaches for in-context learning with few labeled data by a large margin.
- Score: 54.69489315952524
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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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