arXiv:2606.03327v1 Announce Type: cross Abstract: Text-to-SQL systems are typically evaluated by query-level execution correctness, but this terminal signal provides little guidance about which intermediate SQL decision caused success or failure. Token-level dense supervision is also ill-suited: SQL tokens do not align with complete semantic decisions, can penalize execution-equivalent queries, and are difficult to label reliably at scale. We therefore propose CAPER, which automatically derives clause-level supervision via counterfactual intervention on the SQL abstract syntax tree, enabling r
The continuous evolution of AI and natural language processing necessitates more granular and efficient methods for training and evaluating complex systems like Text-to-SQL, moving beyond coarse or overly fine-grained supervision signals.
Improving the accuracy and interpretability of Text-to-SQL systems is crucial for democratizing data access and enhancing the capabilities of various applications that rely on natural language interfaces for database interaction.
This research introduces a more effective method for supervising Text-to-SQL models, potentially leading to more accurate and robust systems that can translate natural language into SQL with greater precision, even for complex queries.
- · AI researchers and developers
- · Database interaction software
- · Businesses leveraging natural language query tools
- · Developers relying on less efficient training methodologies
- · Token-level dense supervision advocates
More accurate and efficient Text-to-SQL models become viable for deployment.
Increased adoption of natural language interfaces for database management across industries.
Reduced need for specialized SQL knowledge for data analysts and business users, leading to broader data accessibility.
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