activity
20182026
most citedNo More Trade-Offs. GPT and Fully Informative Privacy Policies

1 citations · 1 across the 4 of their papers we have counts for

collaborators

6 papers

cs.CY2026

What out-of-the-box LLMs can(t) do in law? A Turing test in Italian exams for lawyers, judges and notaries

Germana Bertoli, Ilaria Amelia Caggiano, Francesca Lagioia +3

The article reports on a blind Turing Test experiment, assessing the performance of out-of-the-box leading LLMs on three Italian legal professional exams: the Bar, Judges and Notar…

cs.CL2025

Challenging the Abilities of Large Language Models in Italian: a Community Initiative

Malvina Nissim, Danilo Croce, Viviana Patti +78

The rapid progress of Large Language Models (LLMs) has transformed natural language processing and broadened its impact across research and society. Yet, systematic evaluation of t…

cs.CL2025

Towards Reliable Retrieval in RAG Systems for Large Legal Datasets

Markus Reuter, Tobias Lingenberg, Rūta Liepiņa +5

Retrieval-Augmented Generation (RAG) is a promising approach to mitigate hallucinations in Large Language Models (LLMs) for legal applications, but its reliability is critically de…

cs.CY20241 cited

No More Trade-Offs. GPT and Fully Informative Privacy Policies

Przemysław Pałka, Marco Lippi, Francesca Lagioia +2

The paper reports the results of an experiment aimed at testing to what extent ChatGPT 3.5 and 4 is able to answer questions regarding privacy policies designed in the new format t…

cs.CY2020

Memory networks for consumer protection:unfairness exposed

Federico Ruggeri, Francesca Lagioia, Marco Lippi +1

Recent work has demonstrated how data-driven AI methods can leverage consumer protection by supporting the automated analysis of legal documents. However, a shortcoming of data-dri…

cs.AI2018

CLAUDETTE: an Automated Detector of Potentially Unfair Clauses in Online Terms of Service

Marco Lippi, Przemyslaw Palka, Giuseppe Contissa +4

Terms of service of on-line platforms too often contain clauses that are potentially unfair to the consumer. We present an experimental study where machine learning is employed to…