MEL: Legal Spanish Language Model
Fuente:
arXiv
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| Autores principales: | , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866917903589179392 |
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| author | Sánchez, David Betancur García, Nuria Aldama Jiménez, Álvaro Barbero Nieto, Marta Guerrero Morales, Patricia Marsà Salas, Nicolás Serrano Hernán, Carlos García Coll, Pablo Haya Ponsoda, Elena Montiel Ibáñez, Pablo Calleja |
| author_facet | Sánchez, David Betancur García, Nuria Aldama Jiménez, Álvaro Barbero Nieto, Marta Guerrero Morales, Patricia Marsà Salas, Nicolás Serrano Hernán, Carlos García Coll, Pablo Haya Ponsoda, Elena Montiel Ibáñez, Pablo Calleja |
| contents | Legal texts, characterized by complex and specialized terminology, present a significant challenge for Language Models. Adding an underrepresented language, such as Spanish, to the mix makes it even more challenging. While pre-trained models like XLM-RoBERTa have shown capabilities in handling multilingual corpora, their performance on domain specific documents remains underexplored. This paper presents the development and evaluation of MEL, a legal language model based on XLM-RoBERTa-large, fine-tuned on legal documents such as BOE (Boletín Oficial del Estado, the Spanish oficial report of laws) and congress texts. We detail the data collection, processing, training, and evaluation processes. Evaluation benchmarks show a significant improvement over baseline models in understanding the legal Spanish language. We also present case studies demonstrating the model's application to new legal texts, highlighting its potential to perform top results over different NLP tasks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_16011 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | MEL: Legal Spanish Language Model Sánchez, David Betancur García, Nuria Aldama Jiménez, Álvaro Barbero Nieto, Marta Guerrero Morales, Patricia Marsà Salas, Nicolás Serrano Hernán, Carlos García Coll, Pablo Haya Ponsoda, Elena Montiel Ibáñez, Pablo Calleja Computation and Language Legal texts, characterized by complex and specialized terminology, present a significant challenge for Language Models. Adding an underrepresented language, such as Spanish, to the mix makes it even more challenging. While pre-trained models like XLM-RoBERTa have shown capabilities in handling multilingual corpora, their performance on domain specific documents remains underexplored. This paper presents the development and evaluation of MEL, a legal language model based on XLM-RoBERTa-large, fine-tuned on legal documents such as BOE (Boletín Oficial del Estado, the Spanish oficial report of laws) and congress texts. We detail the data collection, processing, training, and evaluation processes. Evaluation benchmarks show a significant improvement over baseline models in understanding the legal Spanish language. We also present case studies demonstrating the model's application to new legal texts, highlighting its potential to perform top results over different NLP tasks. |
| title | MEL: Legal Spanish Language Model |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2501.16011 |