33 citations · 34 across the 8 of their papers we have counts for
9 papers
Does Linguistic Structure Enrichment Enhance Coherence Assessment? Not With Current Architectures
Victor Mazzotti, Luiz Pereira, Marina Bitencourt dos Santos +4
Recent advances in large language models have transformed human-computer interaction. Despite their fluency, these models often produce texts that are grammatically correct but sem…
Language Models for Portuguese: A Systematic Mapping Study
Jhessica Silva, Carlos Caetano, Helena Maia +3
In recent years, the rapid development of language models has transformed the field of Natural Language Processing through a wide range of applications. However, the development of…
Evaluation of AI Ethics Tools in Language Models: A Developers' Perspective Case Study
Jhessica Silva, Diego A. B. Moreira, Gabriel O. dos Santos +4
In Artificial Intelligence (AI), language models have gained significant importance due to the widespread adoption of systems capable of simulating realistic conversations with hum…
CACARA: Cross-Modal Alignment Leveraging a Text-Centric Approach for Cost-Effective Multimodal and Multilingual Learning
Diego A. B. Moreira, Alef I. Ferreira, Jhessica Silva +10
As deep learning models evolve, new applications and challenges are rapidly emerging. Tasks that once relied on a single modality, such as text, images, or audio, are now enriched…
Clustering Discourses: Racial Biases in Short Stories about Women Generated by Large Language Models
Gustavo Bonil, João Gondim, Marina dos Santos +5
This study investigates how large language models, in particular LLaMA 3.2-3B, construct narratives about Black and white women in short stories generated in Portuguese. From 2100…
Yet another algorithmic bias: A Discursive Analysis of Large Language Models Reinforcing Dominant Discourses on Gender and Race
Gustavo Bonil, Simone Hashiguti, Jhessica Silva +5
With the advance of Artificial Intelligence (AI), Large Language Models (LLMs) have gained prominence and been applied in diverse contexts. As they evolve into more sophisticated v…
FairPIVARA: Reducing and Assessing Biases in CLIP-Based Multimodal Models
Diego A. B. Moreira, Alef Iury Ferreira, Jhessica Silva +10
Despite significant advancements and pervasive use of vision-language models, a paucity of studies has addressed their ethical implications. These models typically require extensiv…
CAPIVARA: Cost-Efficient Approach for Improving Multilingual CLIP Performance on Low-Resource Languages
Gabriel Oliveira dos Santos, Diego A. B. Moreira, Alef Iury Ferreira +9
This work introduces CAPIVARA, a cost-efficient framework designed to enhance the performance of multilingual CLIP models in low-resource languages. While CLIP has excelled in zero…
Weakly Supervised Attention-based Models Using Activation Maps for Citrus Mite and Insect Pest Classification
Edson Bollis, Helena Maia, Helio Pedrini +1
Citrus juices and fruits are commodities with great economic potential in the international market, but productivity losses caused by mites and other pests are still far from being…