This content provides a comprehensive evaluation of ChatGPT's zero-shot Text-to-SQL capability, meaning its ability to convert natural language into SQL queries without prior examples. It explores the model's performance and limitations in this complex task.
DivSkill-SQL introduces a residual skill optimization framework to build complementary Text-to-SQL ensembles, improving accuracy by targeting marginal contributions to Pass@K. It achieves significant accuracy gains on Spider2-Lite for Snowflake and BigQuery over existing ensemble baselines.
This paper proposes a knowledge-aware Text-to-SQL framework to convert natural language questions into executable SQL queries, even in low-resource settings. It addresses challenges like scarce annotated data and opaque schema definitions by injecting task-specific knowledge into both training and inference.
This research introduces EnterpriseMem-Bench, a novel multi-turn Text-to-SQL benchmark with 300 sessions and 1,400 turns from enterprise domains. It empirically evaluates five frontier models, including GPT and Claude variants, revealing that stateless multi-turn Text-to-SQL models achieve zero execution accuracy by Turn 3.