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 (Ohio State University)
- 2. Kun Qian (Adobe Inc.)
- 3. Siddhartha Sahai (Adobe Inc.)
- 4. Yuan Tian (Purdue University)
- 5. Shaddy Garg (Adobe Inc.)
- 6. Huan Sun (Ohio State University)
- 7. Yunyao Li (Adobe Inc.)
BibTeX Citation
@article{zhang_vldb25,
title = {{EVOSCHEMA: TOWARDS TEXT-TO-SQL ROBUSTNESS AGAINST SCHEMA EVOLUTION}},
author = {Zhang, Tianshu and Qian, Kun and Sahai, Siddhartha and Tian, Yuan and Garg, Shaddy and Sun, Huan and Li, Yunyao},
journal = {PVLDB},
series = {{VLDB} '25},
volume = {18},
number = {10},
pages = {3655--3668},
doi = {10.14778/3748191.3748222},
url = {https://doi.org/10.14778/3748191.3748222},
year = {2025}
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Showing 7 of 7 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 279 | Text-to-SQL Empowered by Large Language Models: A Benchmark Evaluation | 2024 | VLDB | 0.00022468369 |
| 756 | CodeS: Towards Building Open-source Language Models for Text-to-SQL | 2024 | SIGMOD | 0.0001431656 |
| 2,473 | PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel | 2023 | VLDB | 8.5326287e-05 |
| 2,852 | The Dawn of Natural Language to SQL: Are We Fully Ready? | 2024 | VLDB | 8.0455088e-05 |
| 3,354 | MT-Teql: Evaluating and Augmenting Neural NLIDB on Real-world Linguistic and Schema Variations | 2022 | VLDB | 7.4918609e-05 |
| 4,251 | FinSQL: Model-Agnostic LLMs-based Text-to-SQL Framework for Financial Analysis | 2024 | SIGMOD | 6.8031106e-05 |
| 6,101 | LLM for Data Management | 2024 | VLDB | 5.9774273e-05 |
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