arXiv:2607.06799v1 Announce Type: new Abstract: Evaluating uncertainty in AI-generated SQL queries requires estimating whether a query is correct, where correct means it executes to the same result as a human-written reference. We study which signals predict correctness on hard multi-table text-to-SQL, using AUROC to measure how well each ranks correct queries above incorrect ones. On BIRD and Spider, black-box signals such as string, structural, and execution self-consistency, a schema-relevance score, and query executability all fall between about 0.61 and 0.68 AUROC, with string self-consis
The proliferation of AI-generated code, particularly in sensitive areas like database querying, necessitates robust mechanisms for evaluating correctness and reliability.
Improving the predictability of AI-generated SQL query correctness is crucial for deploying autonomous AI agents in enterprise and critical data infrastructure.
This research provides a framework for understanding and identifying effective signals for evaluating AI-generated SQL, paving the way for more reliable and trustworthy AI coding assistants.
- · AI agents developers
- · Data-intensive enterprises
- · AI quality assurance sector
- · Companies with low AI quality standards
- · Manual SQL query validation methods
Increased reliability of Text-to-SQL systems, leading to wider adoption in data management.
Reduced human oversight required for AI-generated database interactions, accelerating AI agent workflows.
Potential for fully autonomous data analysis and manipulation systems with higher degrees of trust and accuracy.
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Read at arXiv cs.LG