Model Releases
The Death of Schema Linking? Text-to-SQL in the Age of Well-Reasoned Language Models
arXiv:2408.07702v2 Announce Type: cross Abstract: Schema linking is a crucial step in Text-to-SQL pipelines. Its goal is to retrieve the relevant tables and columns of a target database for a user's q
arXiv:2408.07702v2 Announce Type: cross Abstract: Schema linking is a crucial step in Text-to-SQL pipelines. Its goal is to retrieve the relevant tables and columns of a target database for a user's query while disregarding irrelevant ones. However, imperfect schema linking can often exclude required columns needed for accurate query generation. In this work, we revisit schema linking when using the latest generation of large language models (LLMs). We find empirically that newer models are adept at utilizing relevant schema elements during generation even in the presence of large numbers of irrelevant ones. As such, our Text-to-SQL pipeline entirely forgoes schema linking in cases where the schema fits within the model's context window in order to minimize issues due to filtering required schema elements. Furthermore, instead of filtering contextual information, we highlight techniques such as augmentation, selection, and correction, and adopt them to improve the accuracy of our Text-to-SQL pipeline. Our approach ranks first on the BIRD benchmark achieving an accuracy of 71.83%.
Related
- Reflect-SQL: A Self-Reflection Based Framework for Text-to-SQL
- TAHOE: Text-to-SQL with Automated Hint Optimization from Experience
- When LLMs Read Tables Carelessly: Measuring and Reducing Data Referencing Errors
- Database Context Compression for Text-to-SQL on Real-World Large Databases
Source: arXiv cs.AI | 2026-09-17