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The article discusses the shortcomings of achieving high accuracy in Text-to-SQL systems, emphasizing that 90% accuracy is insufficient for enterprise applications. It highlights the need for rigorous evaluation frameworks, like Spider 2.0, to ensure reliability and trust in AI-driven analytics.
- 90% accuracy still means 1 in 10 SQL queries is wrong, which is enough to destroy user trust and stall enterprise adoption.
- Standard benchmarks like Spider 1.0 don't reflect enterprise reality; Spider 2.0 tests against schemas with 3,000+ columns to simulate real-world messiness.
- Simple accuracy scores are misleading — metrics like Execution Accuracy (EX) and Soft-F1 are needed to actually capture whether generated SQL is reliable.