# Enhancing Legal Compliance and Regulation Analysis with Large Language Models

arXiv: https://arxiv.org/abs/2404.17522 · HTML: https://mlsanity.com/paper/2404.17522/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 42 references that are in the map.
- First posted on arXiv 26 Apr 2024.

## Builds on (most cited first, 10 of 10)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Retrieval-Augmented Generation for Large Language Models: A Survey](https://mlsanity.com/paper/2312.10997/) (2023)
- [NLP-based Automated Compliance Checking of Data Processing Agreements against GDPR](https://mlsanity.com/paper/2209.09722/) (2022)
- [Automatic Detection of Causality in Requirement Artifacts: the CiRA Approach](https://mlsanity.com/paper/2101.10766/) (2021)
- [AI-enabled Automation for Completeness Checking of Privacy Policies](https://mlsanity.com/paper/2106.05688/) (2021)
- [An Empirical Evaluation of GDPR Compliance Violations in Android mHealth Apps](https://mlsanity.com/paper/2008.05864/) (2020)
- [Using Language Models for Enhancing the Completeness of Natural-language Requirements](https://mlsanity.com/paper/2302.04792/) (2023)
- [A Multi-solution Study on GDPR AI-enabled Completeness Checking of DPAs](https://mlsanity.com/paper/2311.13881/) (2023)
- [Detecting Requirements Smells With Deep Learning: Experiences, Challenges and Future Work](https://mlsanity.com/paper/2108.03087/) (2021)
- [Automated Detection of Typed Links in Issue Trackers](https://mlsanity.com/paper/2206.07182/) (2022)

## Built on by (most cited first, 10 of 10)

- [A Comprehensive Survey on Multi-Agent Cooperative Decision-Making: Scenarios, Approaches, Challenges and Perspectives](https://mlsanity.com/paper/2503.13415/) (2025)
- [GraphCompliance: Structure-Aware Regulatory Compliance through Policy-Context Graph Alignment](https://mlsanity.com/paper/2510.26309/) (2025)
- [CoCoLex: Confidence-guided Copy-based Decoding for Grounded Legal Text Generation](https://mlsanity.com/paper/2508.05534/) (2025)
- [AIReg-Bench: Benchmarking Language Models That Assess AI Regulation Compliance](https://mlsanity.com/paper/2510.01474/) (2025)
- [RepV: Safety-Separable Latent Spaces for Scalable Neurosymbolic Plan Verification](https://mlsanity.com/paper/2510.26935/) (2025)
- [M-PACE: Mother Child Framework for Multimodal Compliance](https://mlsanity.com/paper/2509.15241/) (2025)
- [GreenIQ: A Deep Search Platform for Comprehensive Carbon Market Analysis and Automated Report Generation](https://mlsanity.com/paper/2503.16041/) (2025)
- [The Role of Emotional Stimuli and Intensity in Shaping Large Language Model Behavior](https://mlsanity.com/paper/2604.07369/) (2026)
- [An Approach for a Supporting Multi-LLM System for Automated Certification Based on the German IT-Grundschutz](https://mlsanity.com/paper/2606.25608/) (2026)
- [From Regulation to Implementation: A Critical Evaluation of LLM-Assisted Regulatory Compliance in Industry](https://mlsanity.com/paper/2608.21317/) (2026)

## Abstract (arXiv)

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.