Spider 2.0-AIFunc is a 465-instance benchmark for evaluating text-to-SQL systems on queries that incorporate Snowflake Cortex AI functions, with evaluations of ten models showing proprietary models reach 67-70% accuracy.
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ORM-based test-time verification improves Text-to-SQL accuracy over heuristic selection by up to 4.33% on BIRD and 2.10% on Spider using automated labeling.
GRAB improves multi-table QA performance by encoding relational data as graphs and bridging structural signals to frozen LLMs through latent tokens.
citing papers explorer
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Spider 2.0-AIFunc: Extending Real-World Text-to-SQL to AI-Native SQL Workflows
Spider 2.0-AIFunc is a 465-instance benchmark for evaluating text-to-SQL systems on queries that incorporate Snowflake Cortex AI functions, with evaluations of ten models showing proprietary models reach 67-70% accuracy.
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Test-Time Verification for Text-to-SQL via Outcome Reward Models
ORM-based test-time verification improves Text-to-SQL accuracy over heuristic selection by up to 4.33% on BIRD and 2.10% on Spider using automated labeling.
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Latent Bridges for Multi-Table Question Answering
GRAB improves multi-table QA performance by encoding relational data as graphs and bridging structural signals to frozen LLMs through latent tokens.