isaacus/mteb-barexam-qa

Bar Exam QA (MTEB format)

This is the test split of the Bar Exam QA dataset formatted in the Massive Text Embedding Benchmark (MTEB) information retrieval dataset format.

This dataset is intended to facilitate the consistent and reproducible evaluation of information retrieval models on Bar Exam QA with the mteb embedding model evaluation framework.

More specifically, this dataset tests the ability of information retrieval models to identify legal provisions relevant to US bar exam questions.

This dataset forms part of the Massive Legal Embeddings Benchmark (MLEB), the largest, most diverse, and most comprehensive benchmark for legal text embedding models.

Methodology 🧪

To understand how Bar Exam QA was created, refer to its documentation.

This dataset was formatted by concatenating the prompt and question columns of the source data delimited by a single space (or, where there was no prompt, reverting to the question only) into queries (or anchors), and treating the gold_passage column as relevant (or positive) passages.

Structure 🗂️

As per the MTEB information retrieval dataset format, this dataset comprises three splits, default, corpus and queries.

The default split pairs queries (query-id) with relevant passages (corpus-id), each pair having a score of 1.

The corpus split contains relevant passages from Bar Exam QA, with the text of a passage being stored in the text key and its id being stored in the _id key.

The queries split contains queries, with the text of a query being stored in the text key and its id being stored in the _id key.

License 📜

This dataset is licensed under CC BY SA 4.0.

Citation 🔖

If you use this dataset, please cite the Massive Legal Embeddings Benchmark (MLEB) as well.

@inproceedings{Zheng_2025, series={CSLAW ’25},
   title={A Reasoning-Focused Legal Retrieval Benchmark},
   url={http://dx.doi.org/10.1145/3709025.3712219},
   DOI={10.1145/3709025.3712219},
   booktitle={Proceedings of the Symposium on Computer Science and Law on ZZZ},
   publisher={ACM},
   author={Zheng, Lucia and Guha, Neel and Arifov, Javokhir and Zhang, Sarah and Skreta, Michal and Manning, Christopher D. and Henderson, Peter and Ho, Daniel E.},
   year={2025},
   month=mar, pages={169–193},
   collection={CSLAW ’25},
   eprint={2505.03970}
}

@misc{butler2025massivelegalembeddingbenchmark,
      title={The Massive Legal Embedding Benchmark (MLEB)}, 
      author={Umar Butler and Abdur-Rahman Butler and Adrian Lucas Malec},
      year={2025},
      eprint={2510.19365},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2510.19365}, 
}