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

Nina Narodytska; Shay Vargaftik

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arXiv · 2407.05153

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

Nina Narodytska·Shay Vargaftik

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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

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Nina Narodytska, Shay Vargaftik. 2024-07-06. Lucy: Think and Reason to Solve Text-to-SQL. https://arxiv.org/abs/2407.05153

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