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
Lineage graph
Paper → model → repo connections mined from source citations (Tier-1 exact match).
Why these links exist
- Linked via arxiv authorTianyang Liu →
Spider 2.0-AIFunc: Extending Real-World Text-to-SQL to AI-Native SQL Workflows
- Linked via arxiv authorCanwen Xu →
Spider 2.0-AIFunc: Extending Real-World Text-to-SQL to AI-Native SQL Workflows
- Linked via arxiv authorFangyu Lei →
Spider 2.0-AIFunc: Extending Real-World Text-to-SQL to AI-Native SQL Workflows
- Linked via arxiv authorNikki Lijing Kuang →
Spider 2.0-AIFunc: Extending Real-World Text-to-SQL to AI-Native SQL Workflows
- Linked via arxiv authorJixuan Chen →
Spider 2.0-AIFunc: Extending Real-World Text-to-SQL to AI-Native SQL Workflows
- Linked via arxiv authorTao Yu →
Spider 2.0-AIFunc: Extending Real-World Text-to-SQL to AI-Native SQL Workflows
- Linked via arxiv authorJulian McAuley →
Spider 2.0-AIFunc: Extending Real-World Text-to-SQL to AI-Native SQL Workflows
- Linked via arxiv authorZhewei Yao →
Spider 2.0-AIFunc: Extending Real-World Text-to-SQL to AI-Native SQL Workflows
- Linked via arxiv authorYuxiong He →
Spider 2.0-AIFunc: Extending Real-World Text-to-SQL to AI-Native SQL Workflows