README.md

---
license: cc-by-sa-4.0
task_categories:
- question-answering
language:
- en
tags:
- legal
size_categories:
- n<1K
dataset_info:
  - config_name: default
    features:
      - name: query-id
        dtype: string
      - name: corpus-id
        dtype: string
      - name: score
        dtype: float64
    splits:
    - name: test
      num_examples: 117
  - config_name: corpus
    features:
      - name: _id
        dtype: string
      - name: title
        dtype: string
      - name: text
        dtype: string
    splits:
      - name: corpus
        num_examples: 116
  - config_name: queries
    features:
      - name: _id
        dtype: string
      - name: text
        dtype: string
    splits:
      - name: queries
        num_examples: 117

configs:
  - config_name: default
    data_files:
      - split: test
        path: data/default.jsonl
  - config_name: corpus
    data_files:
      - split: corpus
        path: data/corpus.jsonl
  - config_name: queries
    data_files:
      - split: queries
        path: data/queries.jsonl

---
# Bar Exam QA Benchmark 📝
The dataset includes questions from multistate bar exams and answers sourced from expert annotations.
|  | |
| --- | --- |
| Task category    | t2t |
| Domains     | Legal, Written      |
| Reference  | https://reglab.github.io/legal-rag-benchmarks/       |

This dataset was produced by modifying the reglab ```Bar Exam QA``` dataset, by combining question and prompt text into a single query, and using expert annotated passages as answers.
As a benchmark, this dataset is best designed for legal information retrieval and question answering related tasks.
# Citation
```@inproceedings{zheng2025,
  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.},
  title = {A Reasoning-Focused Legal Retrieval Benchmark},
  year = {2025},
  series = {CSLAW '25 (forthcoming)}
}
```