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| Main Authors: | , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2403.03883 |
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| _version_ | 1866916150011494400 |
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| author | Colombo, Pierre Pires, Telmo Pessoa Boudiaf, Malik Culver, Dominic Melo, Rui Corro, Caio Martins, Andre F. T. Esposito, Fabrizio Raposo, Vera Lúcia Morgado, Sofia Desa, Michael |
| author_facet | Colombo, Pierre Pires, Telmo Pessoa Boudiaf, Malik Culver, Dominic Melo, Rui Corro, Caio Martins, Andre F. T. Esposito, Fabrizio Raposo, Vera Lúcia Morgado, Sofia Desa, Michael |
| contents | In this paper, we introduce SaulLM-7B, a large language model (LLM) tailored for the legal domain. With 7 billion parameters, SaulLM-7B is the first LLM designed explicitly for legal text comprehension and generation. Leveraging the Mistral 7B architecture as its foundation, SaulLM-7B is trained on an English legal corpus of over 30 billion tokens. SaulLM-7B exhibits state-of-the-art proficiency in understanding and processing legal documents. Additionally, we present a novel instructional fine-tuning method that leverages legal datasets to further enhance SaulLM-7B's performance in legal tasks. SaulLM-7B is released under the MIT License. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_03883 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | SaulLM-7B: A pioneering Large Language Model for Law Colombo, Pierre Pires, Telmo Pessoa Boudiaf, Malik Culver, Dominic Melo, Rui Corro, Caio Martins, Andre F. T. Esposito, Fabrizio Raposo, Vera Lúcia Morgado, Sofia Desa, Michael Computation and Language In this paper, we introduce SaulLM-7B, a large language model (LLM) tailored for the legal domain. With 7 billion parameters, SaulLM-7B is the first LLM designed explicitly for legal text comprehension and generation. Leveraging the Mistral 7B architecture as its foundation, SaulLM-7B is trained on an English legal corpus of over 30 billion tokens. SaulLM-7B exhibits state-of-the-art proficiency in understanding and processing legal documents. Additionally, we present a novel instructional fine-tuning method that leverages legal datasets to further enhance SaulLM-7B's performance in legal tasks. SaulLM-7B is released under the MIT License. |
| title | SaulLM-7B: A pioneering Large Language Model for Law |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2403.03883 |