Extractive Schema Linking for Text-to-SQL

• URL: http://arxiv.org/abs/2501.17174v1
• Date: Thu, 23 Jan 2025 19:57:08 GMT
• Title: Extractive Schema Linking for Text-to-SQL
• Authors: Michael Glass, Mustafa Eyceoz, Dharmashankar Subramanian, Gaetano Rossiello, Long Vu, Alfio Gliozzo,
• Abstract summary: Text-to-one is emerging as a practical interface for real world databases. We introduce a new approach to adapt decoder-only LLMs to schema linking.
• Score: 17.757832644216446
• License:
• Abstract: 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.

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