The Massive Legal Embedding Benchmark (MLEB)
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866914107758739456 |
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| author | Butler, Umar Butler, Abdur-Rahman Malec, Adrian Lucas |
| author_facet | Butler, Umar Butler, Abdur-Rahman Malec, Adrian Lucas |
| contents | We present the Massive Legal Embedding Benchmark (MLEB), the largest, most diverse, and most comprehensive open-source benchmark for legal information retrieval to date. MLEB consists of ten expert-annotated datasets spanning multiple jurisdictions (the US, UK, EU, Australia, Ireland, and Singapore), document types (cases, legislation, regulatory guidance, contracts, and literature), and task types (search, zero-shot classification, and question answering). Seven of the datasets in MLEB were newly constructed in order to fill domain and jurisdictional gaps in the open-source legal information retrieval landscape. We document our methodology in building MLEB and creating the new constituent datasets, and release our code, results, and data openly to assist with reproducible evaluations. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2510_19365 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | The Massive Legal Embedding Benchmark (MLEB) Butler, Umar Butler, Abdur-Rahman Malec, Adrian Lucas Computation and Language Artificial Intelligence Information Retrieval We present the Massive Legal Embedding Benchmark (MLEB), the largest, most diverse, and most comprehensive open-source benchmark for legal information retrieval to date. MLEB consists of ten expert-annotated datasets spanning multiple jurisdictions (the US, UK, EU, Australia, Ireland, and Singapore), document types (cases, legislation, regulatory guidance, contracts, and literature), and task types (search, zero-shot classification, and question answering). Seven of the datasets in MLEB were newly constructed in order to fill domain and jurisdictional gaps in the open-source legal information retrieval landscape. We document our methodology in building MLEB and creating the new constituent datasets, and release our code, results, and data openly to assist with reproducible evaluations. |
| title | The Massive Legal Embedding Benchmark (MLEB) |
| topic | Computation and Language Artificial Intelligence Information Retrieval |
| url | https://arxiv.org/abs/2510.19365 |