LEAF-SQL uses level-wise exploration with adaptive fine-graining and dual agents to generate diverse SQL skeletons, reaching 71.6% execution accuracy on the BIRD benchmark and outperforming prior search- and skeleton-based methods.
arXiv preprint arXiv:2505.13271 (2025) Verification-based Text-to-SQL Evaluation with Database Constraints 9
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SpotIt+ uses verification to find realistic counterexample databases that expose discrepancies between generated and gold SQL queries missed by standard test-based evaluation on the BIRD dataset.
DeepEye-SQL applies SDLC-inspired orchestration to Text-to-SQL, achieving 73.5% on BIRD-Dev, 75.07% on BIRD-Test, and 89.8% on Spider-Test with ~30B MoE models.
A selection technique based on separating instances and provenance outperforms baselines for choosing among 2-3 NL2SQL candidates on a BIRD-DEV subset without consistency scores.
citing papers explorer
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LEAF-SQL: Level-wise Exploration with Adaptive Fine-graining for Text-to-SQL Skeleton Prediction
LEAF-SQL uses level-wise exploration with adaptive fine-graining and dual agents to generate diverse SQL skeletons, reaching 71.6% execution accuracy on the BIRD benchmark and outperforming prior search- and skeleton-based methods.
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SpotIt+: Verification-based Text-to-SQL Evaluation with Database Constraints
SpotIt+ uses verification to find realistic counterexample databases that expose discrepancies between generated and gold SQL queries missed by standard test-based evaluation on the BIRD dataset.
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DeepEye-SQL: A Software-Engineering-Inspired Text-to-SQL Framework
DeepEye-SQL applies SDLC-inspired orchestration to Text-to-SQL, achieving 73.5% on BIRD-Dev, 75.07% on BIRD-Test, and 89.8% on Spider-Test with ~30B MoE models.
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Data-aware candidate selection in NL2SQL translation via small separating instances
A selection technique based on separating instances and provenance outperforms baselines for choosing among 2-3 NL2SQL candidates on a BIRD-DEV subset without consistency scores.