7 papers
When Large Language Models Know the Table: A Framework for Assessing Data Contamination in Tabular Datasets
Matteo Silvestri, Fabiano Veglianti, Flavio Giorgi +2
Large language models (LLMs) are increasingly exposed to data contamination, i.e., performance gains driven by prior exposure of test datasets rather than generalization. However,…
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…
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…