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Towards Knowledge-Intensive Text-to-SQL Semantic Parsing with Formulaic Knowledge

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arxiv 2301.01067 v1 pith:WZKJ3AHE submitted 2023-01-03 cs.CL

Longxu Dou , Yan Gao , Xuqi Liu , Mingyang Pan , Dingzirui Wang , Wanxiang Che , Dechen Zhan , Min-Yen Kan
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Jian-Guang Lou
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classification cs.CL
keywords knowledgeformulaicdomaindomain-specificknowledge-intensiveknowsqlparsingproblem
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In this paper, we study the problem of knowledge-intensive text-to-SQL, in which domain knowledge is necessary to parse expert questions into SQL queries over domain-specific tables. We formalize this scenario by building a new Chinese benchmark KnowSQL consisting of domain-specific questions covering various domains. We then address this problem by presenting formulaic knowledge, rather than by annotating additional data examples. More concretely, we construct a formulaic knowledge bank as a domain knowledge base and propose a framework (ReGrouP) to leverage this formulaic knowledge during parsing. Experiments using ReGrouP demonstrate a significant 28.2% improvement overall on KnowSQL.

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  1. LogicCat: A Chain-of-Thought Text-to-SQL Benchmark for Complex Reasoning

    cs.CL 2025-05 conditional novelty 5.0 of 10

    The authors built a 4,038-question text-to-SQL benchmark with chain-of-thought annotations and found that state-of-the-art models reach only 33.20% execution accuracy.

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