58 citations · 128 across the 17 of their papers we have counts for
18 papers
Overview of ROMCIR 2026: The 6th Workshop on Reducing Online Misinformation through Credible Information Retrieval
Marcos Fernández-Pichel, Marinella Petrocchi, Kevin Roitero +1
In the digital online ecosystem, we are surrounded by distinct forms of information pollution, posing significant threats to both individuals and society. Fake news, for instance,…
Exploring Geographic Relative Space in Large Language Models through Activation Patching
Stef De Sabbata, Rahul Baiju, Stefano Mizzaro +1
The increased use of Large Language Models (LLMs) in geography raises substantial questions about the safety of integrating these tools across a wide range of processes and analyse…
Context Shapes LLMs Retrieval-Augmented Fact-Checking Effectiveness
Pietro Bernardelle, Stefano Civelli, Kevin Roitero +1
Large language models (LLMs) show strong reasoning abilities across diverse tasks, yet their performance on extended contexts remains inconsistent. While prior research has emphasi…
The Effect of Document Summarization on LLM-Based Relevance Judgments
Samaneh Mohtadi, Kevin Roitero, Stefano Mizzaro +1
Relevance judgments are central to the evaluation of Information Retrieval (IR) systems, but obtaining them from human annotators is costly and time-consuming. Large Language Model…
Behavior and Representation in Open-Weight Large Language Models for Combinatorial Optimization: From Feature Extraction to Algorithm Selection
Francesca Da Ros, Luca Di Gaspero, Kevin Roitero
Recent advances in Large Language Models (LLMs) open new perspectives for automation in optimization, yet little is known about whether their internal representations capture probl…
On Robustness and Reliability of Benchmark-Based Evaluation of LLMs
Riccardo Lunardi, Vincenzo Della Mea, Stefano Mizzaro +1
Large Language Models (LLMs) effectiveness is usually evaluated by means of benchmarks such as MMLU, ARC-C, or HellaSwag, where questions are presented in their original wording, t…
Political Ideology Shifts in Large Language Models
Pietro Bernardelle, Stefano Civelli, Leon Fröhling +3
Large language models (LLMs) are increasingly deployed in politically sensitive contexts, raising concerns about their susceptibility to ideological biases. In this work, we examin…
Large Language Models for Combinatorial Optimization: A Systematic Review
Francesca Da Ros, Michael Soprano, Luca Di Gaspero +1
This systematic review explores the application of Large Language Models (LLMs) in Combinatorial Optimization (CO). We report our findings using the Preferred Reporting Items for S…
Geospatial Mechanistic Interpretability of Large Language Models
Stef De Sabbata, Stefano Mizzaro, Kevin Roitero
Large Language Models (LLMs) have demonstrated unprecedented capabilities across various natural language processing tasks. Their ability to process and generate viable text and co…
Efficiency and Effectiveness of LLM-Based Summarization of Evidence in Crowdsourced Fact-Checking
Kevin Roitero, Dustin Wright, Michael Soprano +2
Evaluating the truthfulness of online content is critical for combating misinformation. This study examines the efficiency and effectiveness of crowdsourced truthfulness assessment…
Mapping and Influencing the Political Ideology of Large Language Models using Synthetic Personas
Pietro Bernardelle, Leon Fröhling, Stefano Civelli +3
The analysis of political biases in large language models (LLMs) has primarily examined these systems as single entities with fixed viewpoints. While various methods exist for meas…
Data Bias Management
Gianluca Demartini, Kevin Roitero, Stefano Mizzaro
Due to the widespread use of data-powered systems in our everyday lives, concepts like bias and fairness gained significant attention among researchers and practitioners, in both i…
Managing Bias in Human-Annotated Data: Moving Beyond Bias Removal
Gianluca Demartini, Kevin Roitero, Stefano Mizzaro
Due to the widespread use of data-powered systems in our everyday lives, the notions of bias and fairness gained significant attention among researchers and practitioners, in both…
The Many Dimensions of Truthfulness: Crowdsourcing Misinformation Assessments on a Multidimensional Scale
Michael Soprano, Kevin Roitero, David La Barbera +4
Recent work has demonstrated the viability of using crowdsourcing as a tool for evaluating the truthfulness of public statements. Under certain conditions such as: (1) having a bal…
Can the Crowd Judge Truthfulness? A Longitudinal Study on Recent Misinformation about COVID-19
Kevin Roitero, Michael Soprano, Beatrice Portelli +6
Recently, the misinformation problem has been addressed with a crowdsourcing-based approach: to assess the truthfulness of a statement, instead of relying on a few experts, a crowd…
Cheap IR Evaluation: Fewer Topics, No Relevance Judgements, and Crowdsourced Assessments
Kevin Roitero
To evaluate Information Retrieval (IR) effectiveness, a possible approach is to use test collections, which are composed of a collection of documents, a set of description of infor…
The COVID-19 Infodemic: Can the Crowd Judge Recent Misinformation Objectively?
Kevin Roitero, Michael Soprano, Beatrice Portelli +5
Misinformation is an ever increasing problem that is difficult to solve for the research community and has a negative impact on the society at large. Very recently, the problem has…
Can The Crowd Identify Misinformation Objectively? The Effects of Judgment Scale and Assessor's Background
Kevin Roitero, Michael Soprano, Shaoyang Fan +3
Truthfulness judgments are a fundamental step in the process of fighting misinformation, as they are crucial to train and evaluate classifiers that automatically distinguish true a…