Design, Validation, and Risk Assessment of LLM-Based Generative AI Systems Operating in the Legal Sector

Large Language Models (LLMs) are increasingly capable of supporting user decisions and performing various tasks autonomously in a wide range of professional domains, including the legal sector. In this work, we describe the general lifecycle of LLM-based systems and the architecture of a system designed to support legal experts in the process of generating maxims from judgments. We propose three application-specific performance metrics and a general-purpose assessment method suitable for the comparison of different systems in any content generation task. Finally, we discuss the results achieved by the system prototype that, according to a board of magistrates, outperforms human experts in the task of generating maxims from judgments.

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Publication Details

Published
2024-10-16
DOI
https://doi.org/10.1109/isse63315.2024.10741134
Citations
3
Primary Topic
Law, AI, and Intellectual Property
Type
article
Field-Weighted Citation Impact
1.04
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article

Design, Validation, and Risk Assessment of LLM-Based Generative AI Systems Operating in the Legal Sector

Antonio Moschitta, Emanuele Buchicchio, Francesco Santoni, Alessio De Angelis et al.
3 citations
Law, AI, and Intellectual Property
1.04
article

Design, Validation, and Risk Assessment of LLM-Based Generative AI Systems Operating in the Legal Sector

Antonio Moschitta, Emanuele Buchicchio, Francesco Santoni, Alessio De Angelis, Paolo Carbone, Lucio San Marco
article en
3 citations

Abstract

Large Language Models (LLMs) are increasingly capable of supporting user decisions and performing various tasks autonomously in a wide range of professional domains, including the legal sector. In this work, we describe the general lifecycle of LLM-based systems and the architecture of a system designed to support legal experts in the process of generating maxims from judgments. We propose three application-specific performance metrics and a general-purpose assessment method suitable for the comparison of different systems in any content generation task. Finally, we discuss the results achieved by the system prototype that, according to a board of magistrates, outperforms human experts in the task of generating maxims from judgments.

University of Perugia (IT)
Openalex Percentile: Top 18%
Law, AI, and Intellectual Property
1.04
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Design, Validation, and Risk Assessment of LLM-Based Generative AI Systems Operating in the Legal Sector — Antonio Moschitta, Emanuele Buchicchio, et al. · (2024) | TGRS Research Map | TGRS