SaulLM-7B: A pioneering Large Language Model for Law
About
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.
Pierre Colombo, Telmo Pessoa Pires, Malik Boudiaf, Dominic Culver, Rui Melo, Caio Corro, Andre F. T. Martins, Fabrizio Esposito, Vera L\'ucia Raposo, Sofia Morgado, Michael Desa• 2024
Related benchmarks
| Task | Dataset | Result | Rank | |
|---|---|---|---|---|
| Code Generation | HumanEval | -- | 1043 | |
| Reasoning | MMLU-Pro | Accuracy27.57 | 241 | |
| Question Answering | MedMCQA | Accuracy41.5 | 98 | |
| Reasoning | GPQA | Accuracy30.3 | 88 | |
| Medical Reasoning | MedMCQA | Accuracy41.5 | 58 | |
| Reasoning | MMLU | Accuracy55.86 | 54 | |
| Language Understanding | MMLU stratified sampling 50 samples per category | Accuracy55.86 | 14 | |
| Language Understanding | MMLU-Pro stratified sampling: 150 samples per category | Accuracy27.57 | 14 | |
| Legal Inquisitive Dialogue | U.S. Supreme Court Oral Argument dataset | CS Score4.01 | 7 | |
| Dialogue Generation | Judicial Dialogue Human Evaluation (test) | CS3.73 | 6 |
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