The development of domain-specific language models has significantly advanced natural language processing applications in various specialized fields, particularly in biomedicine. However, the focus has largely been on English-language models, leaving a gap for less-resourced languages such as Italian. This paper introduces Igea, the first decoder-only language model designed explicitly for biomedical text generation in Italian. Built on the Minerva model and continually pretrained on a diverse corpus of Italian medical texts, Igea is available in three model sizes: 350 million, 1 billion, and 3 billion parameters. The models aim to balance computational efficiency and performance, addressing the challenges of managing the peculiarities of medical terminology in Italian. We evaluate Igea using a mix of in-domain biomedical corpora and general-purpose benchmarks, highlighting its efficacy and retention of general knowledge even after the domain-specific training. This paper discusses the model's development and evaluation, providing a foundation for future advancements in Italian biomedical NLP.

本文介绍了Igea，这是第一个专为意大利生物医学文本生成而设计的仅解码器语言模型。基于Minerva模型，持续在多样意大利医学文本语料库上进行预训练的Igea有三种模型大小：3.5亿、10亿和30亿参数。我们使用混合的领域内生物医学语料库和通用基准进行Igea的评估，突出了其效果和特定培训后的保留一般知识能力。该论文讨论了模型的开发和评估，并为意大利生物医学自然语言处理的未来发展奠定了基础。

Igea: 适用于意大利生物医学文本生成的仅解码器语言模型