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.
Towards Knowledge-Intensive Text-to-SQL Semantic Parsing with Formulaic Knowledge
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abstract
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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2025 1verdicts
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LogicCat: A Chain-of-Thought Text-to-SQL Benchmark for Complex Reasoning
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.