MilaNLP Lab

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

Previous Next

Meet the team!

Researchers

Avatar

Dirk Hovy

Full Professor

Avatar

Debora Nozza

Assistant Professor

Avatar

Arianna Muti

Postdoc

Avatar

Célia Nouri

Visiting PhD Student

Avatar

Elisa Bassignana

Postdoc

Avatar

Ivana Crescenzi

PhD Student

Avatar

Lorena Calvo-Bartolomé

Postdoc

Avatar

Maite Heredia

Visiting PhD Student

Avatar

Tamara Quiroga Curín

Visiting PhD Student

Avatar

Tanise Ceron

Postdoc

Projects

.js-id-current
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

Quickly discover relevant content by filtering publications.

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

EMNLP 2026
September, 2026
A controlled study of how explicit and implicit demographic cues affect large language model scoring, feedback, and question answering in educational assessment.
PDF Dataset Project DOI

WildSEEK: Evaluating Language Models for Information-Seeking

EMNLP 2026
August, 2026
WildSEEK introduces real-world information-seeking queries and an evaluation framework for assessing the reliability, safety, and fairness of language-model responses.
PDF Dataset Project Project Source Document DOI

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.
PDF Project DOI

SimBench: Benchmarking the Ability of Large Language Models to Simulate Human Behaviors

ICLR 2026
April, 2026
SimBench provides a standardized benchmark for evaluating how faithfully large language models simulate human behavior across diverse social and behavioral tasks.
PDF Project Source Document

Still unsafe: what's holding us back on online safety for women

AI and Ethics
April, 2026
Online Violence Against Women (OVAW) is a persistent and evolving challenge, encompassing AI-generated sexual content, cyberstalking, …
PDF
See all publications