Search Results for author:
Found 10 papers, 4 papers with code
The interplay between domain specialization and model size: a case study in the legal domain
To compare general and specialized training, we filtered a web-based dataset to extract legal domain data.
Sabiá-3 Technical Report
This report presents Sabi\'a-3, our new flagship language model, and Sabiazinho-3, a more cost-effective sibling.
Juru: Legal Brazilian Large Language Model from Reputable Sources
This study contributes to the growing body of scientific evidence showing that pretraining data selection may enhance the performance of large language models, enabling the exploration of these models at a lower cost.
Sabiá-2: A New Generation of Portuguese Large Language Models
We introduce Sabi\'a-2, a family of large language models trained on Portuguese texts.
Evaluating GPT-4's Vision Capabilities on Brazilian University Admission Exams
Recent advancements in language models have showcased human-comparable performance in academic entrance exams.
Sabiá: Portuguese Large Language Models
By evaluating on datasets originally conceived in the target language as well as translated ones, we study the contributions of language-specific pretraining in terms of 1) capturing linguistic nuances and structures inherent to the target language, and 2) enriching the model's knowledge about a domain or culture.
Evaluating GPT-3.5 and GPT-4 Models on Brazilian University Admission Exams
The present study aims to explore the capabilities of Language Models (LMs) in tackling high-stakes multiple-choice tests, represented here by the Exame Nacional do Ensino M\'edio (ENEM), a multidisciplinary entrance examination widely adopted by Brazilian universities.
Sequence-to-Sequence Models for Extracting Information from Registration and Legal Documents
A typical information extraction pipeline consists of token- or span-level classification models coupled with a series of pre- and post-processing scripts.
Knowledge Transfer for Melanoma Screening with Deep Learning
Knowledge transfer impacts the performance of deep learning -- the state of the art for image classification tasks, including automated melanoma screening.
Towards Automated Melanoma Screening: Exploring Transfer Learning Schemes
Deep learning is the current bet for image classification.
