Pedro H. Paiola

According to our database, Pedro H. Paiola authored at least 14 papers between 2022 and 2025.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2025

Evaluating Large Language Models for Brazilian Portuguese Sentiment Analysis: A Comparative Study of Multilingual State-of-the-Art vs. Brazilian Portuguese Fine-Tuned LLMs.

André da Fonseca Schuck, Gabriel Lino Garcia, João Renato Ribeiro Manesco, Pedro Henrique Paiola, João Paulo Papa

J. Braz. Comput. Soc., 2025

The Bode Family of Large Language Models: Investigating the Frontiers of LLMs in Brazilian Portuguese.

Pedro Henrique Paiola, Gabriel Lino Garcia, João Vitor Mariano Correia, João Renato Ribeiro Manesco, Ana Lara Alves Garcia, João Paulo Papa

J. Braz. Comput. Soc., 2025

Abstractive Summarization with LLMs for Texts in Brazilian Portuguese.

Hugo A. P. G. De Camargo, Pedro Henrique Paiola, Gabriel Lino Garcia, João Paulo Papa

J. Braz. Comput. Soc., 2025

Enhancing Governance and Explainability in Large Language Models: A Framework for Interpretability-Driven Decision-Making.

Pedro H. Paiola, Gabriel Lino Garcia, João Renato Ribeiro Manesco, Lucas Miranda, Maria Paola de Salvo, João P. Papa

Proceedings of the 9th IEEE Forum on Research and Technologies for Society and Industry, 2025

A Step Forward for Medical LLMs in Brazilian Portuguese: Establishing a Benchmark and a Strong Baseline.

Gabriel Lino Garcia, João Renato Ribeiro Manesco, Pedro Henrique Paiola, Pedro Henrique Crespan Ribeiro, Ana Lara Alves Garcia, João Paulo Papa

Proceedings of the 38th IEEE International Symposium on Computer-Based Medical Systems, 2025

2024

A Review on Scientific Knowledge Extraction using Large Language Models in Biomedical Sciences.

Gabriel Lino Garcia, João Renato Ribeiro Manesco, Pedro Henrique Paiola, Lucas Miranda, Maria Paola de Salvo, João Paulo Papa

CoRR, 2024

Adapting LLMs for the Medical Domain in Portuguese: A Study on Fine-Tuning and Model Evaluation.

Pedro Henrique Paiola, Gabriel Lino Garcia, João Renato Ribeiro Manesco, Mateus Roder, Douglas Rodrigues, João Paulo Papa

CoRR, 2024

Introducing Bode: A Fine-Tuned Large Language Model for Portuguese Prompt-Based Task.

Gabriel Lino Garcia, Pedro Henrique Paiola, Luis Henrique Morelli, Giovani Candido, Arnaldo Cândido Júnior, Danilo Samuel Jodas, Luis C. S. Afonso, Ivan Rizzo Guilherme, Bruno Elias Penteado, João Paulo Papa

CoRR, 2024

RecognaSumm: A Novel Brazilian Summarization Dataset.

Pedro Henrique Paiola, Gabriel Lino Garcia, Danilo Samuel Jodas, João Vitor Mariano Correia, Luis C. S. Afonso, João Paulo Papa

Proceedings of the 16th International Conference on Computational Processing of Portuguese, 2024

Text Summarization and Temporal Learning Models Applied to Portuguese Fake News Detection in a Novel Brazilian Corpus Dataset.

Gabriel Lino Garcia, Pedro Henrique Paiola, Danilo Samuel Jodas, Luis Afonso Sugi, João Paulo Papa

Proceedings of the 16th International Conference on Computational Processing of Portuguese, 2024

Impact of Quantization on Large Language Models for Portuguese Classification Tasks.

Danilo Samuel Jodas, Gabriel Lino Garcia, Pedro Henrique Paiola, João Renato Ribeiro Manesco, João Paulo Papa

Proceedings of the Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, 2024

GemBode and PhiBode: Adapting Small Language Models to Brazilian Portuguese.

Gabriel Lino Garcia, Pedro Henrique Paiola, Eduardo Garcia, João Renato Ribeiro Manesco, João Paulo Papa

Proceedings of the Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, 2024

Hate Speech Detection in Portuguese Using BERTimbau.

João Otávio Rodrigues Ferreira Frediani, Gabriel Lino Garcia, Pedro Henrique Paiola, Leandro Aparecido Passos, João Paulo Papa, Aparecido Nilceu Marana

Proceedings of the Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, 2024

2022

Deep Learning-Based Abstractive Summarization for Brazilian Portuguese Texts.

Pedro H. Paiola, Gustavo H. de Rosa, João P. Papa

Proceedings of the Intelligent Systems - 11th Brazilian Conference, 2022