MilaNLP Lab

We are the Milan Natural Language Processing Group. We study, develop and deploy NLP algorithms to solve real-world problems.

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Meet the team!

Researchers

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Dirk Hovy

Full Professor

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Debora Nozza

Assistant Professor

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Arianna Muti

Postdoc

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Donya Rooein

Postdoc

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Emanuele Moscato

Postdoc

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Flor Plaza

Postdoc

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Maria Nawrocka

Visiting PhD Student

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Paul Röttger

Postdoc

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Tanise Ceron

Postdoc

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Tiancheng Hu

Visiting PhD Student

Alumni

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Amanda Cercas Curry

Researcher, CENTAI Institute

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Anne Lauscher

Associate Professor, University of Hamburg

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Federico Bianchi

Postdoc, Stanford University

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Tommaso Fornaciari

Direttore Tecnico Superiore della Polizia di Stato

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Giuseppe Attanasio

Postdoc

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Jan Globisz

Master’s student

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Kilian Theil

Researcher Associate, University of Mannheim

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Lorenzo Lupo

Postdoc

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Matthias Orlikowski

PhD Student, Bielefeld University

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Nikita Soni

PhD Student, Stony Brook University

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Pieter Delobelle

PhD Student, KU Leuven

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Pietro Lesci

PhD student, Cambridge University

Projects

INDOMITA

Innovative Demographically-aware Hate Speech Detection in Online Media in Italian

PERSONAE

Personalized and Subjective approaches to Natural Language Processing

MENTALISM

Measuring, Tracking, and Analyzing Inequality using Social Media

INTEGRATOR

Incorporating Demographic Factors into Natural Language Processing Models

Twitter Healthy Conversations

Devising Metrics for Assessing Echo Chambers, Incivility, and Intolerance on Twitter

Recent Twitter activity

Recent Publications

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Divine LLaMAs: Bias, Stereotypes, Stigmatization, and Emotion Representation of Religion in Large Language Models

September, 2024
Emotions play important epistemological and cognitive roles in our lives, revealing our values and guiding our actions. Previous work …
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Compromesso! Italian Many-Shot Jailbreaks Undermine the Safety of Large Language Models

August, 2024
As diverse linguistic communities and users adopt large language models (LLMs), assessing their safety across languages becomes …
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My Answer is C: First-Token Probabilities Do Not Match Text Answers in Instruction-Tuned Language Models

August, 2024
The open-ended nature of language generation makes the evaluation of autoregressive large language models (LLMs) challenging. One …
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Political Compass or Spinning Arrow? Towards More Meaningful Evaluations for Values and Opinions in Large Language Models

August, 2024
Much recent work seeks to evaluate values and opinions in large language models (LLMs) using multiple-choice surveys and …
PDF Project

XSTest: A Test Suite for Identifying Exaggerated Safety Behaviours in Large Language Models

July, 2024
Without proper safeguards, large language models will readily follow malicious instructions and generate toxic content. This risk …
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