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Interactive Text-to-SQL Generation via Editable Step-by-Step Explanations

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arxiv 2305.07372 v5 pith:DOS2J6A6 submitted 2023-05-12 cs.DB cs.CL

Yuan Tian , Zheng Zhang , Zheng Ning , Toby Jia-Jun Li , Jonathan K. Kummerfeld , Tianyi Zhang This is my paper · ORCID
classification cs.DBcs.CL
keywords theymanyusersdatabasesdatasetsissuesmultiplequeries
verification ladder T0 review T1 audit T2 compute T3 formal
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Relational databases play an important role in business, science, and more. However, many users cannot fully unleash the analytical power of relational databases, because they are not familiar with database languages such as SQL. Many techniques have been proposed to automatically generate SQL from natural language, but they suffer from two issues: (1) they still make many mistakes, particularly for complex queries, and (2) they do not provide a flexible way for non-expert users to validate and refine incorrect queries. To address these issues, we introduce a new interaction mechanism that allows users to directly edit a step-by-step explanation of a query to fix errors. Our experiments on multiple datasets, as well as a user study with 24 participants, demonstrate that our approach can achieve better performance than multiple SOTA approaches. Our code and datasets are available at https://github.com/magic-YuanTian/STEPS.

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  1. DCG-SQL: Enhancing In-Context Learning for Text-to-SQL with Deep Contextual Schema Link Graph

    cs.AI 2025-05 conditional novelty 6.0 of 10

    DCG-SQL retrieves text-to-SQL demonstrations by embedding a question-to-schema link graph, improving execution accuracy on Spider by up to about 10 points over random demonstrations on small LLMs.

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