Topic
text-to-sql
Study Reveals 27 Error Types in LLM Text-to-SQL, Introduces MapleDoctor Repair Framework
Researchers conducted the first comprehensive study of errors in LLM-based text-to-SQL systems using in-context learning. They identified 27 error types across 7 categories and proposed MapleDoctor, a detection and repair framework that outperforms existing solutions by repairing 13.8% more queries with negligible mis-repairs and reducing repair latency by 67.4%.
Beyond Text-to-SQL: New Agentic LLM System Governs Enterprise Analytics APIs
Enterprise analytics faces barriers for non-technical users. A new agentic LLM system called Analytic Agent addresses these by translating natural language to secure governed API calls, bypassing raw database access. Evaluated on 90 real enterprise use cases, it validates permissions, executes queries, and generates compliant visualizations.
New Self-Enhanced Fine-Tuning Method Boosts Text-to-SQL Reasoning and Generalization
Researchers propose CoTE-SQL, a self-enhanced fine-tuning method that improves text-to-SQL generation by integrating reasoning traces, structured chain-of-thought prompting, and execution error correction. The approach achieves state-of-the-art results on Bird and Spider benchmarks, particularly on complex queries.