增强法律合规与对大型语言模型的监管分析
Enhancing Legal Compliance and Regulation Analysis with Large Language Models
AI总结：
本研究提出一种基于大型语言模型的法律合规与监管分析智能体框架，利用BERT和GPT模型自动化合规检查，在5000项法律条款数据集上分别达到92.3%和88.7%的准确率，显著优于现有基线。
摘要：
本研究探讨大型语言模型（LLMs）在法律合规与监管分析中的应用。我们引入了法律合规与监管分析智能体框架，该框架利用LLMs自动化合规检查与监管分析任务。我们在包含5000项法律条款的数据集上评估了该框架。结果表明，BERT和GPT模型分别达到了92.3%和88.7%的准确率，显著优于现有基线。研究发现，LLMs能够有效分类法律条款并自动化合规检查，在法律合规与监管分析领域展现出巨大潜力。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
