arXiv:2606.08018v1 Announce Type: new Abstract: Existing text-to-SQL benchmarks are largely centered on SQLite, making it difficult to evaluate whether models can generalize across heterogeneous SQL dialects. However, real-world database systems differ substantially in syntax, functions, type systems, and execution semantics, so the same natural language intent often requires dialect-specific SQL realizations. We introduce UniQL, a human-verified benchmark for cross-dialect text-to-SQL evaluation. UniQL aligns 1,534 natural language questions with executable SQL annotations across 16 SQL diale
The proliferation of AI models interacting with diverse real-world database systems necessitates robust dialect-agnostic text-to-SQL capabilities, a gap highlighted by existing SQLite-centric benchmarks.
This benchmark addresses a crucial limitation in current text-to-SQL models, impacting the practical deployment and reliability of AI agents across heterogeneous enterprise environments.
The introduction of UniQL shifts evaluation standards for text-to-SQL, pushing models towards true dialect universality rather than SQLite-specific optimization.
- · AI Agent developers
- · Database vendors
- · Enterprises with diverse data infrastructure
- · Text-to-SQL models optimized solely for SQLite
- · Legacy natural language interface providers
Improved accuracy and versatility of natural language interfaces for databases.
Faster adoption of AI agents in complex enterprise IT environments.
Enhanced data accessibility for non-technical users across an organization, democratizing data insights.
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Read at arXiv cs.AI