Evoschema: Towards Text-To-Sql Robustness Against Schema Evolution
Summary: EvoSchema: a benchmark and taxonomy of ten schema-evolution perturbations (column/table-level) to systematically test text-to-SQL robustness. Finds table-level edits hurt most; models trained on diverse evolved schemas gain robustness and avoid spurious cues. (summarized by gpt-5-mini on Feb 09 2026)
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Authors
- 1. Tianshu Zhang
- 2. Kun Qian
- 3. Siddhartha Sahai
- 4. Yuan Tian
- 5. Shaddy Garg
- 6. Huan Sun
- 7. Yunyao Li
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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 369 | Text-to-SQL Empowered by Large Language Models: A Benchmark Evaluation | 2024 | VLDB | 0.0002547515 |
| 998 | CodeS: Towards Building Open-source Language Models for Text-to-SQL | 2024 | SIGMOD | 0.00014729379 |
| 2,902 | PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel | 2023 | VLDB | 7.93939e-05 |
| 3,501 | MT-TeQL: Evaluating and Augmenting Neural NLIDB on Real-world Linguistic and Schema Variations | 2022 | VLDB | 7.0366785e-05 |
| 3,662 | The Dawn of Natural Language to SQL: Are We Fully Ready? | 2024 | VLDB | 6.8672143e-05 |
| 5,033 | FinSQL: Model-Agnostic LLMs-based Text-to-SQL Framework for Financial Analysis | 2024 | SIGMOD | 5.7486224e-05 |
| 7,020 | LLM for Data Management | 2024 | VLDB | 4.8595728e-05 |
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