Structured semantic-request generation (A3) achieves 25.67% correctness on enterprise data queries, outperforming direct SQL generation (22.25%) but introducing trade-offs in safety and cost.
Summary
Natural language→SQL translation remains fundamentally hard; this benchmark quantifies the correctness-safety-cost trade-off space, helping teams choose between direct generation, agent-based, and planned architectures when building governed data interfaces.
Why it matters
Natural language→SQL translation remains fundamentally hard; this benchmark quantifies the correctness-safety-cost trade-off space, helping teams choose between direct generation, agent-based, and planned architectures when building governed data interfaces.
Implementation verdict
This is a research benchmark, not a production tool. It replaces guesswork about architecture choice with empirical data: A3 wins on correctness but A1 wins on policy compliance and A4 on cost. Worth studying if you're building semantic layers over enterprise databases, but requires your own testing on real schemas and query patterns.
Sources
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