DTS-SQL: Decomposed Text-to-SQL with Small Large Language Models
<|reference_start|>DTS-SQL: Decomposed Text-to-SQL with Small Large Language Models: Leading models for the text-to-SQL task heavily rely on proprietary Large Language Models (LLMs), posing concerns over data privacy. Closing the performance gap between small open-source models and large proprietary models is crucial to mitigate this reliance. To this end, we introduce a novel two-stage fine-tuning approach that decomposes the task into two simpler tasks. Through comprehensive evaluation on two large cross-domain datasets and two small LLMs, we show that this approach improves execution accuracy by 3 to 7 percent, effectively aligning the performance of open-source models with their proprietary counterparts.<|reference_end|>
@article{pourreza2024dts-sql:,
    title={DTS-SQL: Decomposed Text-to-SQL with Small Large Language Models},
    author={Mohammadreza Pourreza and Davood Rafiei},
    journal={arXiv preprint arXiv:2402.01117},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.01117},
    primaryClass={cs.CL cs.DB cs.HC}
}