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--- |
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license: cc-by-sa-4.0 |
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task_categories: |
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- text-retrieval |
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- question-answering |
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language: |
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- en |
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tags: |
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- legal |
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- law |
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size_categories: |
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- n<1K |
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source_datasets: |
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- reglab/barexam_qa |
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dataset_info: |
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- config_name: default |
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features: |
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- name: query-id |
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dtype: string |
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- name: corpus-id |
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dtype: string |
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- name: score |
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dtype: float64 |
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splits: |
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- name: test |
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num_examples: 117 |
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- config_name: corpus |
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features: |
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- name: _id |
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dtype: string |
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- name: title |
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dtype: string |
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- name: text |
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dtype: string |
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splits: |
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- name: corpus |
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num_examples: 116 |
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- config_name: queries |
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features: |
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- name: _id |
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dtype: string |
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- name: text |
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dtype: string |
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splits: |
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- name: queries |
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num_examples: 117 |
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configs: |
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- config_name: default |
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data_files: |
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- split: test |
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path: data/default.jsonl |
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- config_name: corpus |
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data_files: |
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- split: corpus |
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path: data/corpus.jsonl |
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- config_name: queries |
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data_files: |
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- split: queries |
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path: data/queries.jsonl |
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pretty_name: Bar Exam QA (MTEB format) |
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--- |
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# Bar Exam QA (MTEB format) |
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This is the test split of the [Bar Exam QA](https://huggingface.co/datasets/reglab/barexam_qa) dataset formatted in the [Massive Text Embedding Benchmark (MTEB)](https://github.com/embeddings-benchmark/mteb) information retrieval dataset format. |
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This dataset is intended to facilitate the consistent and reproducible evaluation of information retrieval models on Bar Exam QA with the [`mteb`](https://github.com/embeddings-benchmark/mteb) embedding model evaluation framework. |
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More specifically, this dataset tests the ability of information retrieval models to identify legal provisions relevant to US bar exam questions. |
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This dataset has been processed into the MTEB format by [Isaacus](https://isaacus.com/), a legal AI research company. |
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## Methodology 🧪 |
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To understand how Bar Exam QA was created, refer to its [documentation](https://huggingface.co/datasets/reglab/barexam_qa). |
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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. |
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## Structure 🗂️ |
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As per the MTEB information retrieval dataset format, this dataset comprises three splits, `default`, `corpus` and `queries`. |
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The `default` split pairs queries (`query-id`) with relevant passages (`corpus-id`), each pair having a `score` of 1. |
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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. |
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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. |
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## License 📜 |
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This dataset is licensed under [CC BY SA 4.0](https://choosealicense.com/licenses/cc-by-sa-4.0/). |
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## Citation 🔖 |
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```bibtex |
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@inproceedings{Zheng_2025, series={CSLAW ’25}, |
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title={A Reasoning-Focused Legal Retrieval Benchmark}, |
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url={http://dx.doi.org/10.1145/3709025.3712219}, |
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DOI={10.1145/3709025.3712219}, |
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booktitle={Proceedings of the Symposium on Computer Science and Law on ZZZ}, |
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publisher={ACM}, |
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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.}, |
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year={2025}, |
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month=mar, pages={169–193}, |
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collection={CSLAW ’25}, |
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eprint={2505.03970} |
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} |
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``` |
