5 papers
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…
What do vision-language models see in the context? Investigating multimodal in-context learning
Gabriel O. dos Santos, Esther Colombini, Sandra Avila
In-context learning (ICL) enables Large Language Models (LLMs) to learn tasks from demonstration examples without parameter updates. Although it has been extensively studied in LLM…
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…