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Large Language Model Enhanced Text-to-SQL Generation: A Survey

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arxiv 2410.06011 v1 pith:PLENO6BF submitted 2024-10-08 cs.DB

Xiaohu Zhu , Qian Li , Lizhen Cui , Yongkang Liu This is my paper
classification cs.DB
keywords languagetext-to-sqlsurveylargechangesdevelopmentenhancedgeneration
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Cited by 5 Pith papers

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