Open Generative Large Language Models For Galician

Gamallo Pablo, Rodríguez Pablo, De-dios-flores Iria, Sotelo Susana, Paniagua Silvia, Bardanca Daniel, Pichel José Ramom, Garcia Marcos. Arxiv 2024

[Paper]    
Ethics And Bias GPT Model Architecture Pretraining Methods RAG Training Techniques

Large language models (LLMs) have transformed natural language processing. Yet, their predominantly English-centric training has led to biases and performance disparities across languages. This imbalance marginalizes minoritized languages, making equitable access to NLP technologies more difficult for languages with lower resources, such as Galician. We present the first two generative LLMs focused on Galician to bridge this gap. These models, freely available as open-source resources, were trained using a GPT architecture with 1.3B parameters on a corpus of 2.1B words. Leveraging continual pretraining, we adapt to Galician two existing LLMs trained on larger corpora, thus mitigating the data constraints that would arise if the training were performed from scratch. The models were evaluated using human judgments and task-based datasets from standardized benchmarks. These evaluations reveal a promising performance, underscoring the importance of linguistic diversity in generative models.

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