collaborators

5 papers

cs.CL2026

Topics as Proxies for Sociodemographics: How Conversational Context Affects LLM Answers

Vera Neplenbroek, Gabriele Sarti, Arianna Bisazza +2

When large language models (LLMs) are used in high-stakes scenarios, such as legal, medical and financial advice, even a single conversation history is enough to drive differences…

cs.CL2026

One Persona, Many Cues, Different Results: How Sociodemographic Cues Impact LLM Personalization

Franziska Weeber, Vera Neplenbroek, Jan Batzner +1

Personalization of LLMs by sociodemographic subgroup often improves user experience, but can also introduce or amplify biases and unfair outcomes across groups. Prior work has empl…

cs.CL2025

Reading Between the Prompts: How Stereotypes Shape LLM's Implicit Personalization

Vera Neplenbroek, Arianna Bisazza, Raquel Fernández

Generative Large Language Models (LLMs) infer user's demographic information from subtle cues in the conversation -- a phenomenon called implicit personalization. Prior work has sh…

cs.CL2025

Cross-Lingual Transfer of Debiasing and Detoxification in Multilingual LLMs: An Extensive Investigation

Vera Neplenbroek, Arianna Bisazza, Raquel Fernández

Recent generative large language models (LLMs) show remarkable performance in non-English languages, but when prompted in those languages they tend to express higher harmful social…

cs.CL2025

LLMs instead of Human Judges? A Large Scale Empirical Study across 20 NLP Evaluation Tasks

Anna Bavaresco, Raffaella Bernardi, Leonardo Bertolazzi +17

There is an increasing trend towards evaluating NLP models with LLMs instead of human judgments, raising questions about the validity of these evaluations, as well as their reprodu…