arXiv:2606.05634v1 Announce Type: new Abstract: Real-world text-to-SQL is often under-specified until user phrases are grounded in how the database stores values. Prior work attempts to address this by requiring a semantic layer to specify groundings in advance, but such specifications are often incomplete, especially in expert domains where domain-specific conventions are under-documented. As this leaves multiple grounding hypotheses open for the same SQL part, we introduce GATE (Grouding After Test from Execution), which bootstraps missing groundings from execution feedback. GATE keeps groun
The proliferation of complex databases and the desire for more natural language interaction necessitates improved text-to-SQL solutions that can handle under-specification and domain-specific conventions.
This development allows AI systems to more accurately translate natural language queries into executable database commands, enhancing the utility and accessibility of data for non-technical users and more advanced AI agents.
The ability to bootstrap semantic layers from execution feedback means less manual pre-specification is required, making text-to-SQL systems more robust and adaptable to new domains without extensive human intervention.
- · AI agent developers
- · Database administrators
- · Data analytics platforms
- · Enterprise software
- · Manual SQL query writers (for routine tasks)
- · Consultants specializing in semantic layer definition
AI agents become more capable of directly querying and manipulating enterprise databases with natural language.
Reduced friction in data access accelerates data-driven decision-making and automation across various industries.
The increased reliance on AI for data interaction could expose new vulnerabilities related to data integrity and security, necessitating advanced oversight mechanisms.
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