---
pretty_name: UK Legislative Long Titles
task_categories:
- text-retrieval
- summarization
- text-ranking
tags:
- legal
- law
- legislation
- uk
language:
- en
language_details: en-GB
annotations_creators:
- found
language_creators:
- found
license: cc-by-4.0
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: 78
- config_name: corpus
  features:
  - name: _id
    dtype: string
  - name: title
    dtype: string
  - name: text
    dtype: string
  splits:
  - name: corpus
    num_examples: 78
- config_name: queries
  features:
  - name: _id
    dtype: string
  - name: text
    dtype: string
  splits:
  - name: queries
    num_examples: 78
configs:
- config_name: default
  data_files:
  - split: test
    path: default.jsonl
- config_name: corpus
  data_files:
  - split: corpus
    path: corpus.jsonl
- config_name: queries
  data_files:
  - split: queries
    path: queries.jsonl
---
# UK Legislative Long Titles ⚖️
**UK Legislative Long Titles** by [Isaacus](https://isaacus.com/) is a novel, challenging legal information retrieval evaluation dataset consisting of 78 UK laws and their [long titles](https://en.wikipedia.org/wiki/Short_and_long_titles#Long_titles), succinctly summarizing subject matter, scope, and purpose of legislation.

This dataset is meant to stress test the ability of an information retrieval model to retrieve relevant statutes to short queries describing them.

This dataset forms part of the [Massive Legal Embeddings Benchmark (MLEB)](https://isaacus.com/mleb), the largest, most diverse, and most comprehensive benchmark for legal text embedding models. 

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

The `default` split pairs long titles (`query-id`) with legislation (`corpus-id`), each pair having a `score` of 1.

The `corpus` split contains UK legislation (excluding long titles and preceding metadata), with the text of acts being stored in the `text` key and their IDs being stored in the `_id` key. There is also a `title` column which is deliberately set to an empty string in all cases for compatibility with the [`mteb`](https://github.com/embeddings-benchmark/mteb) library.

The `queries` split contains [long titles](https://en.wikipedia.org/wiki/Short_and_long_titles#Long_titles) in the format 'An Act ...', with the text of a long title being stored in the `text` key and its ID being stored in the `_id` key.

## Methodology 🧪
This dataset was constructed by collecting all publicly available UK laws, converting them into plain text with Inscriptis, cleaning them and removing near duplicates with the simhash algorithm, and then using regex to extract their long titles before removing those long titles and everything preceding them from legislation (in order to force models to focus on representing the core semantics of acts' texts rather than their metadata-rich front matter). Finally, 78 statute-long title pairs were randomly selected for inclusion in this dataset.

## License 📜
This dataset is licensed under [CC BY 4.0](https://choosealicense.com/licenses/cc-by-4.0/) which permits both non-commercial and commercial use of this dataset provided appropriate attribution is made to it.

## Citation 🔖
If you use this dataset, please cite the [Massive Legal Embeddings Benchmark (MLEB)](https://arxiv.org/abs/2510.19365):
```bibtex
@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}, 
}
```