Enhancing Legal Compliance and Regulation Analysis with Large Language Models
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arXiv
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866909183142526976 |
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| author | Hassani, Shabnam |
| author_facet | Hassani, Shabnam |
| contents | This research explores the application of Large Language Models (LLMs) for automating the extraction of requirement-related legal content in the food safety domain and checking legal compliance of regulatory artifacts. With Industry 4.0 revolutionizing the food industry and with the General Data Protection Regulation (GDPR) reshaping privacy policies and data processing agreements, there is a growing gap between regulatory analysis and recent technological advancements. This study aims to bridge this gap by leveraging LLMs, namely BERT and GPT models, to accurately classify legal provisions and automate compliance checks. Our findings demonstrate promising results, indicating LLMs' significant potential to enhance legal compliance and regulatory analysis efficiency, notably by reducing manual workload and improving accuracy within reasonable time and financial constraints. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_17522 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Enhancing Legal Compliance and Regulation Analysis with Large Language Models Hassani, Shabnam Software Engineering Artificial Intelligence This research explores the application of Large Language Models (LLMs) for automating the extraction of requirement-related legal content in the food safety domain and checking legal compliance of regulatory artifacts. With Industry 4.0 revolutionizing the food industry and with the General Data Protection Regulation (GDPR) reshaping privacy policies and data processing agreements, there is a growing gap between regulatory analysis and recent technological advancements. This study aims to bridge this gap by leveraging LLMs, namely BERT and GPT models, to accurately classify legal provisions and automate compliance checks. Our findings demonstrate promising results, indicating LLMs' significant potential to enhance legal compliance and regulatory analysis efficiency, notably by reducing manual workload and improving accuracy within reasonable time and financial constraints. |
| title | Enhancing Legal Compliance and Regulation Analysis with Large Language Models |
| topic | Software Engineering Artificial Intelligence |
| url | https://arxiv.org/abs/2404.17522 |