| --- |
| 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 |
| new_version: llmsql-bench/llmsql-2.0 |
| --- |
| |
| # LLMSQL Benchmark |
|
|
| ⚠️ **A newer version of this dataset is available:** |
| 👉 https://huggingface.co/datasets/llmsql-bench/llmsql-2.0 |
|
|
| 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} |
| } |
| ``` |
