EPI-SQL: Enhancing Text-to-SQL Translation with Error-Prevention Instructions
<|reference_start|>EPI-SQL: Enhancing Text-to-SQL Translation with Error-Prevention Instructions: The conversion of natural language queries into SQL queries, known as Text-to-SQL, is a critical yet challenging task. This paper introduces EPI-SQL, a novel methodological framework leveraging Large Language Models (LLMs) to enhance the performance of Text-to-SQL tasks. EPI-SQL operates through a four-step process. Initially, the method involves gathering instances from the Spider dataset on which LLMs are prone to failure. These instances are then utilized to generate general error-prevention instructions (EPIs). Subsequently, LLMs craft contextualized EPIs tailored to the specific context of the current task. Finally, these context-specific EPIs are incorporated into the prompt used for SQL generation. EPI-SQL is distinguished in that it provides task-specific guidance, enabling the model to circumvent potential errors for the task at hand. Notably, the methodology rivals the performance of advanced few-shot methods despite being a zero-shot approach. An empirical assessment using the Spider benchmark reveals that EPI-SQL achieves an execution accuracy of 85.1\%, underscoring its effectiveness in generating accurate SQL queries through LLMs. The findings indicate a promising direction for future research, i.e. enhancing instructions with task-specific and contextualized rules, for boosting LLMs' performance in NLP tasks.<|reference_end|>
@article{liu2024epi-sql:,
    title={EPI-SQL: Enhancing Text-to-SQL Translation with Error-Prevention
  Instructions},
    author={Xiping Liu, Zhao Tan},
    journal={arXiv preprint arXiv:2404.14453},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.14453},
    primaryClass={cs.CL cs.AI cs.DB}
}