10 citations · 11 across the 7 of their papers we have counts for
8 papers
BLAST: Benchmarking LLMs with ASP-based Structured Testing
Manuel Alejandro Borroto Santana, Erica Coppolillo, Francesco Calimeri +3
Large Language Models (LLMs) have demonstrated remarkable performance across a broad spectrum of tasks, including natural language understanding, dialogue systems, and code generat…
Disrupting Networks: Amplifying Social Dissensus via Opinion Perturbation and Large Language Models
Erica Coppolillo, Giuseppe Manco
We study how targeted content injection can strategically disrupt social networks. Using the Friedkin-Johnsen (FJ) model, we utilize a measure of social dissensus and show that (i)…
Unmasking Conversational Bias in AI Multiagent Systems
Erica Coppolillo, Giuseppe Manco, Luca Maria Aiello
Detecting biases in the outputs produced by generative models is essential to reduce the potential risks associated with their application in critical settings. However, the majori…
CAP: Detecting Unauthorized Data Usage in Generative Models via Prompt Generation
Daniela Gallo, Angelica Liguori, Ettore Ritacco +3
To achieve accurate and unbiased predictions, Machine Learning (ML) models rely on large, heterogeneous, and high-quality datasets. However, this could raise ethical and legal conc…
Algorithmic Drift: A Simulation Framework to Study the Effects of Recommender Systems on User Preferences
Erica Coppolillo, Simone Mungari, Ettore Ritacco +4
Digital platforms such as social media and e-commerce websites adopt Recommender Systems to provide value to the user. However, the social consequences deriving from their adoption…
Relevance meets Diversity: A User-Centric Framework for Knowledge Exploration through Recommendations
Erica Coppolillo, Giuseppe Manco, Aristides Gionis
Providing recommendations that are both relevant and diverse is a key consideration of modern recommender systems. Optimizing both of these measures presents a fundamental trade-of…
LLASP: Fine-tuning Large Language Models for Answer Set Programming
Erica Coppolillo, Francesco Calimeri, Giuseppe Manco +2
Recently, Large Language Models (LLMs) have showcased their potential in various natural language processing tasks, including code generation. However, while significant progress h…
Flexible Generation of Preference Data for Recommendation Analysis
Simone Mungari, Erica Coppolillo, Ettore Ritacco +1
Simulating a recommendation system in a controlled environment, to identify specific behaviors and user preferences, requires highly flexible synthetic data generation models capab…