Lucy: Think and Reason to Solve Text-to-SQL
<|reference_start|>Lucy: Think and Reason to Solve Text-to-SQL: 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<|reference_end|>
@article{narodytska2024lucy:,
    title={Lucy: Think and Reason to Solve Text-to-SQL},
    author={Nina Narodytska, Shay Vargaftik},
    journal={arXiv preprint arXiv:2407.05153},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.05153},
    primaryClass={cs.AI}
}