EvoSchema: Towards Text-to-SQL Robustness Against Schema Evolution
Neural text-to-SQL models, which translate natural language questions (NLQs) into SQL queries given a database schema, have achieved remarkable performance. However, database schemas frequently evolve to meet new requirements. Such schema evolution often leads to performance degradation for models trained on static schemas. Existing work either mainly focuses on simply paraphrasing some syntactic or semantic mappings among NLQ, DB and SQL, or lacks a comprehensive and controllable way to investigate the model robustness issue under the schema evolution, which is insufficient when facing the increasingly complex and rich database schema changes in reality, especially in the LLM era. To add...
| Authors | Tianshu Zhang, Kun Qian, Siddhartha Sahai et al. |
|---|---|
| Primary category | cs.DB |
| Published | 11 March 2026 |
| Last updated at source | 11 March 2026 |
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