MultiLegalPile: A 689GB Multilingual Legal Corpus
Joel Niklaus, Veton Matoshi, Matthias Stürmer, Ilias Chalkidis, Daniel E. Ho
Abstract
Large, high-quality datasets are crucial for training Large Language Models (LLMs). However, so far, few datasets are available for specialized critical domains such as law and the available ones are often small and only in English. To fill this gap, we curate and release MULTILEGALPILE, a 689GB corpus in 24 languages from 17 jurisdictions. MULTILE-GALPILE includes diverse legal data sources and allows for pretraining NLP models under fair use, with most of the dataset licensed very permissively. We pretrain two RoBERTa models and one Longformer multilingually, and 24 monolingual models on each of the languagespecific subsets and evaluate them on LEX-TREME. Additionally, we evaluate the English and multilingual models on LexGLUE. Our multilingual models set a new SotA on LEX-TREME and our English models on LexGLUE. We release the dataset, trained models, and all code under the most open licenses possible.
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Install the CLIlune papers fulltext a1bcacaf-1647-4a81-8f5e-b1d9fa9ed441Cited by top-tier papers7
- SaulLM-54B & SaulLM-141B: Scaling Up Domain Adaptation for the Legal DomainPierre Colombo, Telmo Pessoa Pires, Malik Boudiaf, Rui Melo et al.NeurIPS 2024 · 58 citations
- BLADE: Enhancing Black-Box Large Language Models with Small Domain-Specific ModelsHaitao Li, Qingyao Ai, Jia Chen, Qian Dong et al.AAAI 2025 · 6 citations
- LexCLiPR: Cross-Lingual Paragraph Retrieval from Legal JudgmentsRohit Upadhya, T. Y. S. S. SantoshACL 2025 · 3 citations
- ProMALex: Progressive Modular Adapters for Multi-Jurisdictional Legal Language ModelingT. Y. S. S. Santosh, Mohamed Hesham ElganayniACL 2025 · 2 citations
- JurisBench: A Deep Benchmark for Assessing Large Language Models in Professional Legal PracticeZiang Chen, Guannan Li, Fanlin Ji, Yipeng Kang et al.ACL 2026
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- Training language models to follow instructions with human feedbackLong Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida et al.NeurIPS 2022 · 24,707 citations
- Measuring Massive Multitask Language UnderstandingDan Hendrycks, Collin Burns, Steven Basart, Andy Zou et al.ICLR 2021 · 7,905 citations
- Deberta: decoding-Enhanced Bert with Disentangled AttentionPengcheng He, Xiaodong Liu, Jianfeng Gao, Weizhu ChenICLR 2021 · 3,729 citations
- MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained TransformersWenhui Wang, Furu Wei, Li Dong, Hangbo Bao et al.NeurIPS 2020 · 2,727 citations
- Unsupervised Cross-lingual Representation Learning at ScaleAlexis Conneau, Kartikay Khandelwal, Naman Goyal, Vishrav Chaudhary et al.ACL 2020 · 539 citations
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