NLP-based Regulatory Compliance -- Using GPT 4.0 to Decode Regulatory Documents

TL;DR

This paper evaluates GPT-4.0's effectiveness in analyzing regulatory documents for inconsistencies, demonstrating its potential to improve compliance processes through semantic analysis and conflict detection.

Contribution

It introduces a methodology for using GPT-4.0 to detect conflicts in regulatory documents, validated with curated datasets and expert validation.

Findings

GPT-4.0 achieves high precision and recall in conflict detection.

The approach shows promise for automating compliance analysis.

Further fine-tuning can improve accuracy in real-world applications.

Abstract

Large Language Models (LLMs) such as GPT-4.0 have shown significant promise in addressing the semantic complexities of regulatory documents, particularly in detecting inconsistencies and contradictions. This study evaluates GPT-4.0's ability to identify conflicts within regulatory requirements by analyzing a curated corpus with artificially injected ambiguities and contradictions, designed in collaboration with architects and compliance engineers. Using metrics such as precision, recall, and F1 score, the experiment demonstrates GPT-4.0's effectiveness in detecting inconsistencies, with findings validated by human experts. The results highlight the potential of LLMs to enhance regulatory compliance processes, though further testing with larger datasets and domain-specific fine-tuning is needed to maximize accuracy and practical applicability. Future work will explore automated conflict resolution and real-world implementation through pilot projects with industry partners.