Journal article 406 views
Fine-tuning GPT-3 for legal rule classification
Davide Liga 
,
Livio Robaldo
,
Livio Robaldo
Computer Law & Security Review, Volume: 51, Start page: 105864
Swansea University Author:
Livio Robaldo
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...
| Published in: | Computer Law & Security Review |
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| ISSN: | 0267-3649 |
| Published: |
Elsevier BV
2023
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| Online Access: |
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| 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. |
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| Keywords: |
Rule classification, GPT-3, AI&Law |
| College: |
Faculty of Humanities and Social Sciences |
| Start Page: |
105864 |