17 papers
Definitional Sensitivity in Media Bias Detection: A Multi-Definition Dataset and Benchmark
Martin Wessel, Timo Spinde, Jürgen Pfeffer +1
Media bias detection relies on definitions and examples that specify what counts as bias, yet these specifications often vary across datasets or remain implicit, even when given th…
Persona Conditioning as an Assessor-Sensitivity Probe for LLM-Based IR Evaluation
Samaneh Mohtadi, Pietro Bernardelle, Joel Mackenzie +1
Large language models (LLMs) are increasingly used as relevance assessors in information retrieval (IR) evaluation, raising questions about how assessor framing affects judgment re…
LLMs Encode Relevance as a Layer-Wise Cross-Lingual Signal
Pietro Bernardelle, Samaneh Mohtadi, Stefano Civelli +2
Large language models (LLMs) are increasingly used in information retrieval (IR) pipelines as relevance judges and re-rankers. Yet most analyses remain output-centric, evaluating g…
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…
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…
Political Advertising on Facebook During the 2022 Australian Federal Election: A Social Identity Perspective
Stefano Civelli, Pietro Bernardelle, Frank Mols +1
The spread of targeted advertising on social media platforms has revolutionized political marketing strategies. Monitoring these digital campaigns is essential for maintaining tran…