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cs.CL2026

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…

cs.CL2026

A Shared Geometry of Difficulty in Multilingual Language Models

Stefano Civelli, Pietro Bernardelle, Nicolò Brunello +1

Predicting problem-difficulty in large language models (LLMs) refers to estimating how difficult a task is according to the model itself, typically by training linear probes on its…

cs.CL2025

Ideology-Based LLMs for Content Moderation

Stefano Civelli, Pietro Bernardelle, Nardiena A. Pratama +1

Large language models (LLMs) are increasingly used in content moderation systems, where ensuring fairness and neutrality is essential. In this study, we examine how persona adoptio…

cs.CL2025

SubData: Bridging Heterogeneous Datasets to Enable Theory-Driven Evaluation of Political and Demographic Perspectives in LLMs

Pietro Bernardelle, Leon Fröhling, Stefano Civelli +1

As increasingly capable large language models (LLMs) emerge, researchers have begun exploring their potential for subjective tasks. While recent work demonstrates that LLMs can be…

cs.CL2025

Political Ideology Shifts in Large Language Models

Pietro Bernardelle, Stefano Civelli, Leon Fröhling +3

Large language models (LLMs) are increasingly deployed in politically sensitive settings, raising concerns about their potential to encode, amplify, or be steered toward specific i…

cs.CL2025

Towards Detecting Persuasion on Social Media: From Model Development to Insights on Persuasion Strategies

Elyas Meguellati, Stefano Civelli, Pietro Bernardelle +3

Political advertising plays a pivotal role in shaping public opinion and influencing electoral outcomes, often through subtle persuasive techniques embedded in broader propaganda s…