# Extractive Schema Linking for Text-to-SQL

arXiv: https://arxiv.org/abs/2501.17174 · HTML: https://mlsanity.com/paper/2501.17174/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Jan 2025.

## Built on by (most cited first, 13 of 13)

- [Text2SQL-Flow: A Robust SQL-Aware Data Augmentation Framework for Text-to-SQL](https://mlsanity.com/paper/2511.10192/) (2025)
- [Enhancing Text2Cypher with Schema Filtering](https://mlsanity.com/paper/2505.05118/) (2025)
- [JOLT-SQL: Joint Loss Tuning of Text-to-SQL with Confusion-aware Noisy Schema Sampling](https://mlsanity.com/paper/2505.14305/) (2025)
- [Rethinking Schema Linking: A Context-Aware Bidirectional Retrieval Approach for Text-to-SQL](https://mlsanity.com/paper/2510.14296/) (2025)
- [SchemaGraphSQL: Efficient Schema Linking with Pathfinding Graph Algorithms for Text-to-SQL on Large-Scale Databases](https://mlsanity.com/paper/2505.18363/) (2025)
- [Rationalization Models for Text-to-SQL](https://mlsanity.com/paper/2502.06759/) (2025)
- [Schema-First Retrieval: Embedding Catalogs for Natural Language Analytics](https://mlsanity.com/paper/2606.28387/) (2026)
- [GradeSQL: Test-Time Inference with Outcome Reward Models for Text-to-SQL Generation from Large Language Models](https://mlsanity.com/paper/2509.01308/) (2025)
- [Cluster Workload Allocation: Semantic Soft Affinity Using Natural Language Processing](https://mlsanity.com/paper/2601.09282/) (2026)
- [SemanticAgent: A Semantics-Aware Framework for Text-to-SQL Data Synthesis](https://mlsanity.com/paper/2604.21414/) (2026)
- [ACE-SQL: Adaptive Co-Optimization via Empirical Credit Assignment for Text-to-SQL](https://mlsanity.com/paper/2606.05906/) (2026)
- [SAFAARI: Schema-Aware Framework for Accelerated Advertiser Response Intelligence](https://mlsanity.com/paper/2607.25042/) (2026)
- [BudgetSchemaBench: A Budget-Swept Diagnostic for Schema Context in Text-to-SQL](https://mlsanity.com/paper/2610.00092/) (2026)

## Abstract (arXiv)

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.