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Fine-tuning GPT-3 for legal rule classification

Davide Liga Orcid Logo, Livio Robaldo Orcid Logo

Computer Law & Security Review, Volume: 51, Start page: 105864

Swansea University Author: Livio Robaldo Orcid Logo

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DOI (Published version): 10.1016/j.clsr.2023.105864

Abstract

In this paper, we propose a Legal Rule Classification (LRC) task using one of the most discussed language model in the field of Artificial Intelligence, namely GPT-3, a generative pretrained language model. We train and test the proposed LRC task on the GDPR encoded in LegalDocML (Palmirani and Vita...

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Published in: Computer Law & Security Review
ISSN: 0267-3649
Published: Elsevier BV 2023
Online Access: Check full text

URI: https://cronfa.swan.ac.uk/Record/cronfa64410
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Abstract: In this paper, we propose a Legal Rule Classification (LRC) task using one of the most discussed language model in the field of Artificial Intelligence, namely GPT-3, a generative pretrained language model. We train and test the proposed LRC task on the GDPR encoded in LegalDocML (Palmirani and Vitali, 2011) and LegalRuleML (Athan et al., 2013), two widely used XML standards for the legal domain. We use the LegalDocML and LegalRuleML annotations provided in Robaldo et al. (2020) to fine-tuned GPT-3. While showing the ability of large language models (LLMs) to easily learn to classify legal and deontic rules even on small amount of data, we show that GPT-3 can significantly outperform previous experiments on the same task. Our work focused on a multiclass task, showing that GPT-3 is capable to recognize the difference between obligation rules, permission rules and constitutive rules with performances that overcome previous scores in LRC.
Keywords: Rule classification, GPT-3, AI&Law
College: Faculty of Humanities and Social Sciences
Start Page: 105864

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