11 papers
From Plausible to Actionable: A Position on LLM Self-Explanations
Elize Herrewijnen, Benedetta Muscato, Gizem Gezici +1
Large Language Models (LLMs) can generate natural language explanations that rationalize their own decisions, a phenomenon commonly referred to as self-explanations.Such explanatio…
Explainable AI for Cancer Drug Response Prediction: Beyond Univariate Feature Attributions
Martino Ciaperoni, Margherita Lalli, Simone Piaggesi +6
Predicting cancer drug response from transcriptomic profiles is a cornerstone of precision oncology, yet the scientific value of machine learning models hinges not solely on predic…
Learning by Surprise: Adaptive Mitigation of Model Collapse in Large Language Models
Daniele Gambetta, Gizem Gezici, Fosca Giannotti +3
As AI-generated content increasingly populates the web, generative AI models are at growing risk of being trained on their own outputs, a process known as AI autophagy. This feedba…
Disagreeing Rationales: Rethinking Classification and Explainability Evaluation in Hate Speech Detection
Benedetta Muscato, Beiduo Chen, Gizem Gezici +2
Human disagreement is ubiquitous and well-known in labeling. However, variation in explanations, captured through token-level human rationales, remains far less explored. At the sa…
Comparing Explanations is Not Enough, Explain the Change: New Standards are Needed to Explain Behavioral Shifts in Large Language Models
Martino Ciaperoni, Marzio Di Vece, Roberto Pellungrini +3
Large-scale foundation models exhibit \emph{behavioral shifts} when subjected to interventions such as scaling, fine-tuning, reinforcement learning with human feedback, or in-conte…
Perspectives in Play: A Multi-Perspective Approach for More Inclusive NLP Systems
Benedetta Muscato, Lucia Passaro, Gizem Gezici +1
In the realm of Natural Language Processing (NLP), common approaches for handling human disagreement consist of aggregating annotators' viewpoints to establish a single ground trut…