Significant strides have been made in natural language tasks, largely
attributed to the emergence of powerful large language models (LLMs). These
models, pre-trained on extensive and diverse corpora, have become increasingly
capable of comprehending the intricacies of language. Despite the abundance of
LLMs for many high-resource languages, the availability of such models remains
limited for European Portuguese. We introduce Gl\'orIA, a robust European
Portuguese decoder LLM. To pre-train Gl\'orIA, we assembled a comprehensive
PT-PT text corpus comprising 35 billion tokens from various sources. We present
our pre-training methodology, followed by an assessment of the model's
effectiveness on multiple downstream tasks. Additionally, to evaluate our
models' language modeling capabilities, we introduce CALAME-PT (Context-Aware
LAnguage Modeling Evaluation for Portuguese), the first Portuguese zero-shot
language-modeling benchmark. Evaluation shows that Gl\'orIA significantly
outperforms existing open PT decoder models in language modeling and that it
can generate sound, knowledge-rich, and coherent PT-PT text. The model also
exhibits strong potential for various downstream tasks.

通过 Gl'orIA 这一强大的欧洲葡萄牙语解码器 LLM，在多个下游任务中，Gl'orIA 在语言模型和生成有丰富知识且连贯的葡萄牙语文本方面远优于现有的 PT 解码模型。

GlórIA - 一种面向葡萄牙语的生成式和开放性大型语言模型

GlórIA - A Generative and Open Large Language Model for Portuguese

Textual health records of cancer patients are usually protracted and highly
unstructured, making it very time-consuming for health professionals to get a
complete overview of the patient's therapeutic course. As such limitations can
lead to suboptimal and/or inefficient treatment procedures, healthcare
providers would greatly benefit from a system that effectively summarizes the
information of those records. With the advent of deep neural models, this
objective has been partially attained for English clinical texts, however, the
research community still lacks an effective solution for languages with limited
resources. In this paper, we present the approach we developed to extract
procedures, drugs, and diseases from oncology health records written in
European Portuguese. This project was conducted in collaboration with the
Portuguese Institute for Oncology which, besides holding over $10$ years of
duly protected medical records, also provided oncologist expertise throughout
the development of the project. Since there is no annotated corpus for
biomedical entity extraction in Portuguese, we also present the strategy we
followed in annotating the corpus for the development of the models. The final
models, which combined a neural architecture with entity linking, achieved
$F_1$ scores of $88.6$, $95.0$, and $55.8$ per cent in the mention extraction
of procedures, drugs, and diseases, respectively.

采用深度神经模型，结合命名实体识别，并参考葡萄牙肿瘤研究所的专家意见，文中提出了一种从欧洲葡萄牙语癌症病历中提取手术程序，药物和疾病的策略，并取得了较好的性能表现。

一种用于葡萄牙语肿瘤健康记录的生物医学实体提取流水线

A Biomedical Entity Extraction Pipeline for Oncology Health Records in  Portuguese

In this paper we present the first neural-based machine translation system
trained to translate between standard national varieties of the same language.
We take the pair Brazilian - European Portuguese as an example and compare the
performance of this method to a phrase-based statistical machine translation
system. We report a performance improvement of 0.9 BLEU points in translating
from European to Brazilian Portuguese and 0.2 BLEU points when translating in
the opposite direction. We also carried out a human evaluation experiment with
native speakers of Brazilian Portuguese which indicates that humans prefer the
output produced by the neural-based system in comparison to the statistical
system.

本文介绍了第一个基于神经网络的机器翻译系统，用于翻译同一语言的标准国别语言种。我们以巴西 - 欧洲葡萄牙语为例，将该方法的性能与短语统计机器翻译系统进行比较。结果显示，在从欧洲葡萄牙语翻译到巴西葡萄牙语时，BLEU 分数提高了 0.9 分，在相反方向翻译时提高了 0.2 分。我们还进行了一项面向巴西葡萄牙语母语者的人工评估实验，结果表明与统计系统相比，人们更喜欢神经系统生成的输出。