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

• URL: http://arxiv.org/abs/2412.20602v1
• Date: Sun, 29 Dec 2024 22:14:59 GMT
• Title: NLP-based Regulatory Compliance -- Using GPT 4.0 to Decode Regulatory Documents
• Authors: Bimal Kumar, Dmitri Roussinov,
• Abstract summary: This study evaluates GPT-4.0's ability to identify conflicts within regulatory requirements. Using metrics such as precision, recall, and F1 score, the experiment demonstrates GPT-4.0's effectiveness in detecting inconsistencies.
• Score: 0.0
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• 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.

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