NLP-based Regulatory Compliance -- Using GPT 4.0 to Decode Regulatory Documents
| Autor: | Kumar, Bimal, Roussinov, Dmitri |
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| Rok vydání: | 2024 |
| Předmět: | |
| Druh dokumentu: | Working Paper |
| Popis: | 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. Comment: accepted for presentation at Georg Nemetschek Institute Symposium & Expo on Artificial Intelligence for the Built World - Munich, Germany. 12 Sept 2024 |
| Databáze: | arXiv |
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