4 papers
Mixture-of-Expert Blocks Contain Strong Hallucination Detection Signals
Joao Fonseca, Rodrigo Rodrigues, Paolo Romano
Despite their widespread use, Large Language Models (LLMs) remain limited by a fundamental problem: the generation of plausible but false content, known as hallucinations. Most exi…
Uncertainty Estimation by Human Perception versus Neural Models
Pedro Mendes, Paolo Romano, David Garlan
Modern neural networks (NNs) often achieve high predictive accuracy but are poorly calibrated, producing overconfident predictions even when wrong. This miscalibration poses seriou…
CLUE: Neural Networks Calibration via Learning Uncertainty-Error alignment
Pedro Mendes, Paolo Romano, David Garlan
Reliable uncertainty estimation is critical for deploying neural networks (NNs) in real-world applications. While existing calibration techniques often rely on post-hoc adjustments…
Error-Driven Uncertainty Aware Training
Pedro Mendes, Paolo Romano, David Garlan
Neural networks are often overconfident about their predictions, which undermines their reliability and trustworthiness. In this work, we present a novel technique, named Error-Dri…