Text-to-SQL-References

This repository is designed to maintain a curated list of references related Text-to-SQL including State-Of-The-Art, other repositories, code samples and innovative techniques

Repository Structure

• dados_datasus/: DataSUS ingestion, schema, and JDBC-based access (see its README for DB tunnel/test steps).
• analises/: Exploratory data analysis notebooks.
• assets/: Images and supporting assets.
• paraphrase-benchmark/: Benchmark configs for paraphrase evaluation.
• README.ipynb: Notebook version of the README/content.
• ParaphraseEvaluator.py, generated_queries.py, sample.py: Supporting scripts.

Views

• Nascimento, E.R., García, G., Izquierdo, Y.T. et al.
  LLM-Based Text-to-SQL for Real-World Databases.
  SN Computer Science, 6, 130 (2025).
  Paper | Summary

• Nascimento, E., García, G., Feijó, L., Victorio, W., Izquierdo, Y., R. de Oliveira, A., Coelho, G., Lemos, M., Garcia, R., Leme, L., and Casanova, M.
  Text-to-SQL Meets the Real-World.
  In Proceedings of the 26th International Conference on Enterprise Information Systems (2024).
  Paper | Summary

• Zeshun You, Jiebin Yao, Dong Cheng, Zhiwei Wen, Zhiliang Lu, and Xianyi Shen.
  V-SQL: A View-Based Two-Stage Text-to-SQL Framework.
  (2024).
  Paper | Summary

Surveys

• Yuyu Luo, Guoliang Li, Ju Fan, Chengliang Chai, and Nan Tang.
  Natural Language to SQL: State of the Art and Open Problems.
  PVLDB, 18(12): 5466–5471, 2025.
  Paper | Summary

• Ali Mohammadjafari, Anthony S. Maida, and Raju Gottumukkala.
  From Natural Language to SQL: Review of LLM-based Text-to-SQL Systems.
  (2025).
  Paper | Summary

Data Lakes

• Chen, Albert, et al.
  Text-to-SQL for Enterprise Data Analytics.
  arXiv preprint arXiv:2507.14372 (2025).
  Paper | Summary

AutoViz

• Dibia, V.
  LIDA: A Tool for Automatic Generation of Grammar-Agnostic Visualizations and Infographics Using Large Language Models.
  arXiv preprint arXiv:2303.02927 (2023).
  Paper | Summary

• Zhang, R., & Elhamod, M.
  Data-to-Dashboard: Multi-Agent LLM Framework for Insightful Visualization in Enterprise Analytics.
  arXiv preprint arXiv:2505.23695 (2025).
  Paper | Summary

Others

• Coelho, G.M.C. et al.
  Improving the Accuracy of Text-to-SQL Tools Based on Large Language Models for Real-World Relational Databases.
  In: Strauss, C., Amagasa, T., Manco, G., Kotsis, G., Tjoa, A.M., Khalil, I. (eds) Database and Expert Systems Applications. (2024).
  Paper | Summary

• Catalina Dragusin∗, Katsiaryna Mirylenka∗, Christoph Miksovic Czasch, Michael Glass, Nahuel Defosse, Paolo Scotton, and Thomas Gschwind.
  Grounding LLMs for Database Exploration: Intent Scoping and Paraphrasing for Robust NL2SQL.
  VLDB 2025 Workshop.
  Paper | Summary

• Haoyang Li, Jing Zhang, Hanbing Liu, Ju Fan, Xiaokang Zhang, Jun Zhu, Renjie Wei, Hongyan Pan, Cuiping Li, and Hong Chen.
  CodeS: Towards Building Open-source Language Models for Text-to-SQL.
  (2024).
  Paper | Summary

• Cao, Zhenbiao, et al.
  Rsl-sql: Robust schema linking in text-to-sql generation.
  arXiv preprint arXiv:2411.00073 (2024).
  Paper | Summary

• Uber.
  QueryGPT – Natural Language to SQL Using Generative AI.
  (2024).
  Post | Summary

• Biswal, Asim, et al.
  Text2SQL is Not Enough: Unifying AI and Databases with TAG.
  arXiv preprint arXiv:2408.14717 (2024).
  Paper | Summary