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

arXiv: https://arxiv.org/abs/2412.20602 · HTML: https://mlsanity.com/paper/2412.20602/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Dec 2024.

## Built on by (most cited first, 4 of 4)

- [AIReg-Bench: Benchmarking Language Models That Assess AI Regulation Compliance](https://mlsanity.com/paper/2510.01474/) (2025)
- [Benchmarking graph construction by large language models for coherence-driven inference](https://mlsanity.com/paper/2502.13953/) (2025)
- [Automatic coherence-driven inference on arguments](https://mlsanity.com/paper/2509.18523/) (2025)
- [Think Inside the Chunk: RegulaRAG for Regulation-Compliant Scenario Generation using LLMs: A Case Study of UN Regulation No. 152](https://mlsanity.com/paper/2608.16394/) (2026)

## Abstract (arXiv)

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.