4 citations · 4 across the 7 of their papers we have counts for
5 papers · 1 filter
Enhancing XAI Narratives through Multi-Narrative Refinement and Knowledge Distillation
Flavio Giorgi, Matteo Silvestri, Cesare Campagnano +2
Explainable Artificial Intelligence has become a crucial area of research, aiming to demystify the decision-making processes of deep learning models. Among various explainability t…
Generalizability vs. Counterfactual Explainability Trade-Off
Fabiano Veglianti, Flavio Giorgi, Fabrizio Silvestri +1
In this work, we investigate the relationship between model generalization and counterfactual explainability in supervised learning. We introduce the notion of -valid…
Emergent Abilities in Large Language Models: A Survey
Leonardo Berti, Flavio Giorgi, Gjergji Kasneci
Large Language Models (LLMs) are leading a new technological revolution as one of the most promising research streams toward artificial general intelligence. The scaling of these m…
COMBINEX: A Unified Counterfactual Explainer for Graph Neural Networks via Node Feature and Structural Perturbations
Flavio Giorgi, Fabrizio Silvestri, Gabriele Tolomei
Counterfactual explanations have emerged as a powerful tool to unveil the opaque decision-making processes of graph neural networks (GNNs). However, existing techniques primarily f…
Countering Overfitting with Counterfactual Examples
Flavio Giorgi, Fabiano Veglianti, Fabrizio Silvestri +1
Overfitting is a well-known issue in machine learning that occurs when a model struggles to generalize its predictions to new, unseen data beyond the scope of its training set. Tra…