Lucy: Think and Reason to Solve Text-to-SQL

Nina Narodytska, shay vargaftik

Keywords: Large Language Models, Automated Reasoning, Text-to-SQL

TL;DR: Integrating language understanding and automated reasoning for solving Text-to-SQL

Abstract: Large Language Models (LLMs) have made significant progress in assisting users to query databases in natural language. While LLM-based techniques provide state-of-the-art results on many standard benchmarks, their performance significantly drops when applied to large enterprise databases. The reason is that these databases have a large number of tables with complex relationships that are challenging for LLMs to reason about. We analyze challenges that LLMs face in these settings and propose a new solution that combines the power of LLMs in understanding questions with automated reasoning techniques to handle complex database constraints. Based on these ideas, we have developed a new framework that outperforms state-of-the-art techniques in zero-shot text-to-SQL on complex benchmarks

Primary Area: Natural language processing

Submission Number: 14824