5 papers
Iterative In-Context Learning to Enhance LLMs Abstract Reasoning: The Case-Study of Algebraic Tasks
Stefano Fioravanti, Matteo Zavatteri, Roberto Confalonieri +4
LLMs face significant challenges in systematic generalization, particularly when dealing with reasoning tasks requiring compositional rules and handling out-of-distribution example…
Discovering Generalizable Governing Equations for Graph Dynamical Systems with Interpretable Neural Networks
Riccardo Cappi, Paolo Frazzetto, Nicolò Navarin +1
The discovery of symbolic governing equations is a central goal in science; yet, it remains challenging particularly for graph dynamical systems, where the network topology further…
From Text to Talent: A Pipeline for Extracting Insights from Candidate Profiles
Paolo Frazzetto, Muhammad Uzair Ul Haq, Flavia Fabris +1
The recruitment process is undergoing a significant transformation with the increasing use of machine learning and natural language processing techniques. While previous studies ha…
Logic Explanation of AI Classifiers by Categorical Explaining Functors
Stefano Fioravanti, Francesco Giannini, Paolo Frazzetto +2
The most common methods in explainable artificial intelligence are post-hoc techniques which identify the most relevant features used by pretrained opaque models. Some of the most…
Exact Computation of Any-Order Shapley Interactions for Graph Neural Networks
Maximilian Muschalik, Fabian Fumagalli, Paolo Frazzetto +5
Albeit the ubiquitous use of Graph Neural Networks (GNNs) in machine learning (ML) prediction tasks involving graph-structured data, their interpretability remains challenging. In…