Large Language Model Enhanced Text-to-SQL Generation: A Survey
<|reference_start|>Large Language Model Enhanced Text-to-SQL Generation: A Survey: Text-to-SQL translates natural language queries into Structured Query Language (SQL) commands, enabling users to interact with databases using natural language. Essentially, the text-to-SQL task is a text generation task, and its development is primarily dependent on changes in language models. Especially with the rapid development of Large Language Models (LLMs), the pattern of text-to-SQL has undergone significant changes. Existing survey work mainly focuses on rule-based and neural-based approaches, but it still lacks a survey of Text-to-SQL with LLMs. In this paper, we survey the large language model enhanced text-to-SQL generations, classifying them into prompt engineering, fine-tuning, pre-trained, and Agent groups according to training strategies. We also summarize datasets and evaluation metrics comprehensively. This survey could help people better understand the pattern, research status, and challenges of LLM-based text-to-SQL generations.<|reference_end|>
@article{zhu2024large,
    title={Large Language Model Enhanced Text-to-SQL Generation: A Survey},
    author={Xiaohu Zhu, Qian Li, Lizhen Cui, Yongkang Liu},
    journal={arXiv preprint arXiv:2410.06011},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.06011},
    primaryClass={cs.DB}
}