57 citations · 90 across the 14 of their papers we have counts for
15 papers
Wiki-Talkie: Multilingual Benchmarking of Persona-Based Agents on Real-World Discussions
Dennis Fucci, Andrea Bacciu, Dong Liu +2
LLMs are increasingly deployed as autonomous agents in social environments, making it critical to study their ability to faithfully simulate human interactions. Central to this is…
QUORUM: QUality-Optimized Routing Using Multiple annotators
Antonio Purificato, Maria Sofia Bucarelli, Andrea Bacciu +2
Data annotation remains a central bottleneck in natural language processing, requiring human effort to obtain high-quality labels at scale. While Large Language Models (LLMs) offer…
Estimating Uncertainty from Reasoning: A Large-Scale Study of Multi- and Crosslingual MCQA Performance in LLMs
Andrea Alfarano, Andrea Bacciu, Saab Mansour +2
Uncertainty estimation (UE) enables LLM-powered systems to recognize when to abstain, yet existing research has predominantly focused on English. We present the first large-scale e…
Select, Label, Evaluate: Active Testing in NLP
Antonio Purificato, Maria Sofia Bucarelli, Andrea Bacciu +2
Human annotation cost and time remain significant bottlenecks in Natural Language Processing (NLP), with test data annotation being particularly expensive due to the stringent requ…
Monte Carlo Temperature: a robust sampling strategy for LLM's uncertainty quantification methods
Nicola Cecere, Andrea Bacciu, Ignacio Fernández Tobías +1
Uncertainty quantification (UQ) in Large Language Models (LLMs) is essential for their safe and reliable deployment, particularly in critical applications where incorrect outputs c…
The Majority Vote Paradigm Shift: When Popular Meets Optimal
Antonio Purificato, Maria Sofia Bucarelli, Anil Kumar Nelakanti +3
Reliably labelling data typically requires annotations from multiple human workers. However, humans are far from being perfect. Hence, it is a common practice to aggregate labels g…
STLight: a Fully Convolutional Approach for Efficient Predictive Learning by Spatio-Temporal joint Processing
Andrea Alfarano, Alberto Alfarano, Linda Friso +3
Spatio-Temporal predictive Learning is a self-supervised learning paradigm that enables models to identify spatial and temporal patterns by predicting future frames based on past f…
A Reproducible Analysis of Sequential Recommender Systems
Filippo Betello, Antonio Purificato, Federico Siciliano +4
Sequential Recommender Systems (SRSs) have emerged as a highly efficient approach to recommendation systems. By leveraging sequential data, SRSs can identify temporal patterns in u…
Handling Ontology Gaps in Semantic Parsing
Andrea Bacciu, Marco Damonte, Marco Basaldella +1
The majority of Neural Semantic Parsing (NSP) models are developed with the assumption that there are no concepts outside the ones such models can represent with their target symbo…
Generating Query Recommendations via LLMs
Andrea Bacciu, Enrico Palumbo, Andreas Damianou +2
Query recommendation systems are ubiquitous in modern search engines, assisting users in producing effective queries to meet their information needs. However, these systems require…
RRAML: Reinforced Retrieval Augmented Machine Learning
Andrea Bacciu, Florin Cuconasu, Federico Siciliano +3
The emergence of large language models (LLMs) has revolutionized machine learning and related fields, showcasing remarkable abilities in comprehending, generating, and manipulating…
Fauno: The Italian Large Language Model that will leave you senza parole!
Andrea Bacciu, Giovanni Trappolini, Andrea Santilli +2
This paper presents Fauno, the first and largest open-source Italian conversational Large Language Model (LLM). Our goal with Fauno is to democratize the study of LLMs in Italian,…
Integrating Item Relevance in Training Loss for Sequential Recommender Systems
Andrea Bacciu, Federico Siciliano, Nicola Tonellotto +1
Sequential Recommender Systems (SRSs) are a popular type of recommender system that learns from a user's history to predict the next item they are likely to interact with. However,…
Renormalized Graph Representations for Node Classification
Francesco Caso, Giovanni Trappolini, Andrea Bacciu +2
Graph neural networks process information on graphs represented at a given resolution scale. We analyze the effect of using different coarse-grained graph resolutions, obtained thr…
Study on Transfer Learning Capabilities for Pneumonia Classification in Chest-X-Rays Image
Danilo Avola, Andrea Bacciu, Luigi Cinque +3
Over the last year, the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) and its variants have highlighted the importance of screening tools with high diagnostic accura…