Year: 2024
Type: preprint
Abstract: 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.
Source: arXiv (Cornell University)
Authors: 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
Institutions: Université Paris-Saclay, CentraleSupélec, Mathématiques et Informatique pour la Complexité et les Systèmes, Centre National de la Recherche Scientifique, Sorbonne Université, Institut Systèmes Intelligents et de Robotique, University of Lisbon, Instituto Superior Técnico, Universidade Nova de Lisboa
Language: English
Field: Social Sciences
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W4392619035