NLP-based Regulatory Compliance -- Using GPT 4.0 to Decode Regulatory Documents
This addresses the challenge of regulatory compliance for architects and compliance engineers, though it is incremental as it applies an existing method to new data with further testing needed.
This study tackled the problem of identifying conflicts in regulatory documents by evaluating GPT-4.0's ability to detect inconsistencies and contradictions in a curated corpus, achieving results validated by human experts with metrics like precision, recall, and F1 score.
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.