arXiv:2607.06229v1 Announce Type: cross Abstract: 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 platfor
Cloud data platforms are rapidly integrating LLM capabilities as native SQL functions, necessitating new benchmarks to evaluate the generation of AI-native SQL.
The shift to AI-native SQL workflows merges AI capabilities directly into data analytics, fundamentally altering how data is queried and analyzed, making AI accessible to a broader user base.
Traditional text-to-SQL benchmarks are now insufficient, and models will be evaluated on their ability to generate complex AI-enhanced SQL, expanding the scope of data interaction.
- · Cloud data platforms (Snowflake)
- · AI model developers
- · Data analysts
- · Companies with large datasets
- · Legacy SQL tooling
- · Businesses slow to adopt AI-native workflows
Increased efficiency and sophistication in data querying and analysis through embedded AI functions.
Democratization of advanced AI capabilities within existing data infrastructure, reducing the need for specialized AI engineering teams for many tasks.
Potential for a new wave of data-driven applications built directly on AI-native SQL, impacting business intelligence and operational workflows.
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Read at arXiv cs.AI