activity
20232025
most citedLegal Summarisation through LLMs: The PRODIGIT Project

4 citations · 6 across the 5 of their papers we have counts for

collaborators

5 papers

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.AI20251 cited

Foundations for Risk Assessment of AI in Protecting Fundamental Rights

Antonino Rotolo, Beatrice Ferrigno, Jose Miguel Angel Garcia Godinez +2

This chapter introduces a conceptual framework for qualitative risk assessment of AI, particularly in the context of the EU AI Act. The framework addresses the complexities of lega…

cs.CY2024

Lessons Learned in Performing a Trustworthy AI and Fundamental Rights Assessment

Marjolein Boonstra, Frédérick Bruneault, Subrata Chakraborty +21

This report shares the experiences, results and lessons learned in conducting a pilot project ``Responsible use of AI'' in cooperation with the Province of Friesland, Rijks ICT Gil…

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.CL20234 cited

Legal Summarisation through LLMs: The PRODIGIT Project

Thiago Dal Pont, Federico Galli, Andrea Loreggia +3

We present some initial results of a large-scale Italian project called PRODIGIT which aims to support tax judges and lawyers through digital technology, focusing on AI. We have fo…