TRUST-SQL: Tool-Integrated Multi-Turn Reinforcement Learning for Text-to-SQL over Unknown Schemas
#Text-to-SQL #Reinforcement Learning #Database Schemas #AI Tools #Query Generation
📌 Key Takeaways
- TRUST-SQL introduces a tool-integrated multi-turn reinforcement learning approach for Text-to-SQL tasks.
- It addresses challenges in generating SQL queries over unknown database schemas.
- The method leverages reinforcement learning to improve query accuracy through iterative interactions.
- Integration of tools enhances adaptability and performance in real-world database environments.
📖 Full Retelling
arXiv:2603.16448v1 Announce Type: new
Abstract: Text-to-SQL parsing has achieved remarkable progress under the Full Schema Assumption. However, this premise fails in real-world enterprise environments where databases contain hundreds of tables with massive noisy metadata. Rather than injecting the full schema upfront, an agent must actively identify and verify only the relevant subset, giving rise to the Unknown Schema scenario we study in this work. To address this, we propose TRUST-SQL (Truth
🏷️ Themes
AI Research, Database Querying
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Original Source
arXiv:2603.16448v1 Announce Type: new
Abstract: Text-to-SQL parsing has achieved remarkable progress under the Full Schema Assumption. However, this premise fails in real-world enterprise environments where databases contain hundreds of tables with massive noisy metadata. Rather than injecting the full schema upfront, an agent must actively identify and verify only the relevant subset, giving rise to the Unknown Schema scenario we study in this work. To address this, we propose TRUST-SQL (Truth
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