8 citations · 11 across the 10 of their papers we have counts for
12 papers
A CaLLM Judge is a Better Judge: Mitigating Bias in LLM-as-a-judge
Cesare Barbera, Luca Stradiotti, Lorenzo Cascioli +6
Large language models (LLMs) are increasingly used as judges to predict which of two candidate answers a human evaluator would prefer, providing a scalable alternative when human e…
Should I stay or should I show? Learning to selectively disclose information
Carlotta Giacchetta, Alessando Bogani, Cesare Barbera +4
In many high-stakes settings, human decision-makers can acquire support information before making a decision. However, acquiring information is costly, and disclosure may fail to i…
To Ask or Not to Ask: Learning to Require Human Feedback
Andrea Pugnana, Giovanni De Toni, Cesare Barbera +3
Developing decision-support systems that complement human performance in classification tasks remains an open challenge. A popular approach, Learning to Defer (LtD), allows a Machi…
Hyperdimensional Probe: Decoding LLM Representations via Vector Symbolic Architectures
Marco Bronzini, Carlo Nicolini, Bruno Lepri +2
Despite their capabilities, Large Language Models (LLMs) remain opaque with limited understanding of their internal representations. Current interpretability methods either focus o…
You Don't Bring Me Flowers: Mitigating Unwanted Recommendations Through Conformal Risk Control
Giovanni De Toni, Erasmo Purificato, Emilia Gómez +3
Recommenders are significantly shaping online information consumption. While effective at personalizing content, these systems increasingly face criticism for propagating irrelevan…
GNNs Meet Sequence Models Along the Shortest-Path: an Expressive Method for Link Prediction
Francesco Ferrini, Veronica Lachi, Antonio Longa +2
Graph Neural Networks (GNNs) often struggle to capture the link-specific structural patterns crucial for accurate link prediction, as their node-centric message-passing schemes ove…
Community Aware Temporal Network Generation
Nicolò Alessandro Girardini, Antonio Longa, Gaia Trebucchi +3
The advantages of temporal networks in capturing complex dynamics, such as diffusion and contagion, has led to breakthroughs in real world systems across numerous fields. In the ca…
Exploiting Preference Elicitation in Interactive and User-centered Algorithmic Recourse: An Initial Exploration
Seyedehdelaram Esfahani, Giovanni De Toni, Bruno Lepri +3
Algorithmic Recourse aims to provide actionable explanations, or recourse plans, to overturn potentially unfavourable decisions taken by automated machine learning models. In this…
Unveiling LLMs: The Evolution of Latent Representations in a Dynamic Knowledge Graph
Marco Bronzini, Carlo Nicolini, Bruno Lepri +2
Large Language Models (LLMs) demonstrate an impressive capacity to recall a vast range of factual knowledge. However, understanding their underlying reasoning and internal mechanis…
Can LLMs Correct Physicians, Yet? Investigating Effective Interaction Methods in the Medical Domain
Burcu Sayin, Pasquale Minervini, Jacopo Staiano +1
We explore the potential of Large Language Models (LLMs) to assist and potentially correct physicians in medical decision-making tasks. We evaluate several LLMs, including Meditron…
Glitter or Gold? Deriving Structured Insights from Sustainability Reports via Large Language Models
Marco Bronzini, Carlo Nicolini, Bruno Lepri +2
Over the last decade, several regulatory bodies have started requiring the disclosure of non-financial information from publicly listed companies, in light of the investors' increa…
Meta-Path Learning for Multi-relational Graph Neural Networks
Francesco Ferrini, Antonio Longa, Andrea Passerini +1
Existing multi-relational graph neural networks use one of two strategies for identifying informative relations: either they reduce this problem to low-level weight learning, or th…