---
datasets:
- llmsql-bench/llmsql-benchmark
tags:
- text-to-sql
- benchmark
- evaluation
license: mit
language:
- en
bibtex:
- >-
  @article{pihulski2025llmsql, title={LLMSQL: Upgrading WikiSQL for the LLM Era
  of Text-to-SQL}, author={Dzmitry Pihulski and Karol Charchut and Viktoria
  Novogrodskaia and Jan Kocoń}, journal={arXiv preprint arXiv:2510.02350},
  year={2025}, url={https://arxiv.org/abs/2510.02350} }
task_categories:
- question-answering
- text-generation
pretty_name: LLMSQL Benchmark
size_categories:
- 10K<n<100K
repository: https://github.com/LLMSQL/llmsql-benchmark
---

# LLMSQL Benchmark

This benchmark is designed to evaluate text-to-SQL models. For usage of this benchmark see `https://github.com/LLMSQL/llmsql-benchmark`.

Arxiv Article: https://arxiv.org/abs/2510.02350

## Files

- `tables.jsonl` — Database table metadata
- `questions.jsonl` — All available questions
- `train_questions.jsonl`, `val_questions.jsonl`, `test_questions.jsonl` — Data splits for finetuning, see `https://github.com/LLMSQL/llmsql-benchmark`
- `sqlite_tables.db` — sqlite db with tables from `tables.jsonl`, created with the help of `create_db_sql`.
- `create_db.sql` — SQL script that creates the database `sqlite_tables.db`.


`test_output.jsonl` is **not included** in the dataset.

## Citation
If you use this benchmark, please cite:

```
@inproceedings{llmsql_bench,
  title={LLMSQL: Upgrading WikiSQL for the LLM Era of Text-to-SQLels},
  author={Pihulski, Dzmitry and  Charchut, Karol and Novogrodskaia, Viktoria and Koco{'n}, Jan},
  booktitle={2025 IEEE International Conference on Data Mining Workshops (ICDMW)},
  year={2025},
  organization={IEEE}
}
```