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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Célia Nouri

Visiting PhD Student

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Elisa Bassignana

Postdoc

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Ivana Crescenzi

PhD Student

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Lorena Calvo-Bartolomé

Postdoc

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Maite Heredia

Visiting PhD Student

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Tamara Quiroga Curín

Visiting PhD Student

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Tanise Ceron

Postdoc

Projects

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Current Projects Past Projects

SALMON

Social Awareness for better Large Language Model Learning

TOLD

Thinking Out Loud - A Speech-Based Data Collection Framework

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 BlueSky activity

Recent Publications

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What Is The Political Content in LLMs' Pre- and Post-Training Data?

arXiv:2509.22367
October, 2026
Large language models (LLMs) reflect politically-slanted opinions in their generated text. Even though it is widely assumed that model …
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Democratizing News Recommenders: Modeling Multiple Perspectives for News Candidate Generation with VQ-VAE

FAccT 2026
October, 2026
News Recommender Systems (NRS) play a central role in society They shape what users read, whose perspectives they encounter, and …
PDF Project

WildSEEK: Evaluating Language Models for Information-Seeking

EMNLP 2026
October, 2026
Language models are increasingly mediating information access to end users, urging a systematic evaluation of their responses for a …
PDF Project

The Role of Implicit and Explicit Demographic Signals in Large Language Model-based Student Assessment

arXiv
September, 2026
A controlled study of how explicit and implicit demographic cues affect large language model scoring, feedback, and question answering in educational assessment.
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Responsible Evaluation of AI for Mental Health

ACL 2026
July, 2026
An interdisciplinary framework aligns mental-health AI evaluation with clinical validity, user experience, safety, and equity.
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