Extractive Schema Linking for Text-to-SQL
This addresses the challenge of handling large real-world schemas in text-to-SQL systems, which is incremental as it builds on existing schema linking methods.
The paper tackles the problem of efficiently identifying relevant database schema portions for text-to-SQL queries by introducing an extractive approach using decoder-only LLMs, which improves computational efficiency and accuracy compared to generative methods, with fine-grained control over precision-recall trade-offs.
Text-to-SQL is emerging as a practical interface for real world databases. The dominant paradigm for Text-to-SQL is cross-database or schema-independent, supporting application schemas unseen during training. The schema of a database defines the tables, columns, column types and foreign key connections between tables. Real world schemas can be large, containing hundreds of columns, but for any particular query only a small fraction will be relevant. Placing the entire schema in the prompt for an LLM can be impossible for models with smaller token windows and expensive even when the context window is large enough to allow it. Even apart from computational considerations, the accuracy of the model can be improved by focusing the SQL generation on only the relevant portion of the database. Schema linking identifies the portion of the database schema useful for the question. Previous work on schema linking has used graph neural networks, generative LLMs, and cross encoder classifiers. We introduce a new approach to adapt decoder-only LLMs to schema linking that is both computationally more efficient and more accurate than the generative approach. Additionally our extractive approach permits fine-grained control over the precision-recall trade-off for schema linking.