What Predicts Correctness in Text-to-SQL? A Selective-Prediction Study
📰 ArXiv cs.AI
Learn how to predict correctness in text-to-SQL queries using selective-prediction study and evaluate uncertainty in AI-generated SQL queries
Action Steps
- Evaluate uncertainty in AI-generated SQL queries using AUROC metric
- Use black-box signals such as string, structural, and execution self-consistency to predict correctness
- Apply schema-relevant signals to improve prediction accuracy
- Compare the performance of different signals on multi-table text-to-SQL datasets like BIRD and Spider
- Test the robustness of the predictive model on various datasets and scenarios
Who Needs to Know This
Data scientists and AI engineers working on natural language processing and database query generation can benefit from this study to improve the accuracy of their models
Key Insight
💡 Black-box signals and schema-relevant signals can predict correctness in text-to-SQL queries
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📊 Improve text-to-SQL query accuracy with selective-prediction study! 🚀
Key Takeaways
Learn how to predict correctness in text-to-SQL queries using selective-prediction study and evaluate uncertainty in AI-generated SQL queries
Full Article
Title: What Predicts Correctness in Text-to-SQL? A Selective-Prediction Study
Abstract:
arXiv:2607.06799v1 Announce Type: cross 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-releva
Abstract:
arXiv:2607.06799v1 Announce Type: cross 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-releva
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