Papers by Gabriel Prenassi
Instance-Selection-Inspired Undersampling Strategies for Bias Reduction in Small and Large Language Models for Binary Text Classification (2025.acl-long)
Copied to clipboard
Guilherme Fonseca, Washington Cunha, Gabriel Prenassi, Marcos André Gonçalves, Leonardo Chaves Dutra Da Rocha
| Challenge: | Existing methods to mitigate class imbalanced datasets are limited by existing methods. |
| Approach: | They propose two undersampling methods inspired by state-of-the-art Instance Selection techniques to mitigate class imbalance bias in ATC. |
| Outcome: | The proposed methods reduce classifier bias (56%) across all datasets without effectiveness loss while improving efficiency (1.6x speedup), scalability and reducing carbon emissions (up to 50%). |
When High Accuracy Hides Poor Calibration: Rethinking Confidence Evaluation in Transformer-Based Text Classification with Balanced Brier Score (2026.acl-long)
Copied to clipboard
Guilherme Fonseca, Gabriel Prenassi, Washington Cunha, Leonardo Chaves Dutra da Rocha, Marcos André Gonçalves
| Challenge: | Existing evidence for TC under fine-tuning is limited. |
| Approach: | They propose a calibration method that balances the contribution of correct and incorrect predictions within confidence bins. |
| Outcome: | The proposed calibration measures show that the models are overconfident even when miscalibrated . the proposed calibration methods challenge calibration assessment practices and provide a more reliable alternative for evaluating confidence quality in Transformer-based TC. |