Spider 2.0-AIFunc: Extending Real-World Text-to-SQL to AI-Native SQL Workflows
Major cloud data platforms now expose large language model capabilities as native SQL functions, enabling analysts to perform classification, filtering, sentiment analysis, extraction, similarity search, and aggregation within ordinary SQL queries. Yet existing text-to-SQL benchmarks evaluate only conventional SQL and provide no signal on whether models can generate such AI-native SQL. We introduce Spider 2.0-AIFunc, a benchmark of 465 verified instances across 125 real-world databases covering six types of AI functions on the Snowflake platform. Starting from an existing enterprise text-to-SQ
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Paper → model → repo connections mined from source citations (Tier-1 exact match).
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- PossiblePossibly related (embedding) · 66%spiceai/spiceai →
- PossiblePossibly related (embedding) · 64%osmanuygar/sqlatte →
- PossiblePossibly related (embedding) · 54%deepset-ai/haystack-core-integrations →
- PossiblePossibly related (embedding) · 53%AI-powered BI with Snowflake and Amazon Quick →
- PossiblePossibly related (embedding) · 52%dataease/SQLBot →
- PossiblePossibly related (embedding) · 54%Any text-to-SQL benchmark should address difficulties of real-world data stores →
- LinkedLinked via arxiv author · 85%Tianyang Liu →
“Spider 2.0-AIFunc: Extending Real-World Text-to-SQL to AI-Native SQL Workflows”
- LinkedLinked via arxiv author · 85%Canwen Xu →
“Spider 2.0-AIFunc: Extending Real-World Text-to-SQL to AI-Native SQL Workflows”