ODIN: A NL2SQL Recommender to Handle Schema Ambiguity
This addresses a major challenge for users in enterprise environments with complex databases, though it is incremental as it builds on existing LLM advancements.
The paper tackles schema ambiguity in NL2SQL systems by introducing ODIN, a recommendation engine that generates multiple potential SQL queries based on ambiguous schema interpretations, improving the likelihood of correct SQL generation by 1.5-2 times compared to baselines.
NL2SQL (natural language to SQL) systems translate natural language into SQL queries, allowing users with no technical background to interact with databases and create tools like reports or visualizations. While recent advancements in large language models (LLMs) have significantly improved NL2SQL accuracy, schema ambiguity remains a major challenge in enterprise environments with complex schemas, where multiple tables and columns with semantically similar names often co-exist. To address schema ambiguity, we introduce ODIN, a NL2SQL recommendation engine. Instead of producing a single SQL query given a natural language question, ODIN generates a set of potential SQL queries by accounting for different interpretations of ambiguous schema components. ODIN dynamically adjusts the number of suggestions based on the level of ambiguity, and ODIN learns from user feedback to personalize future SQL query recommendations. Our evaluation shows that ODIN improves the likelihood of generating the correct SQL query by 1.5-2$\times$ compared to baselines.