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.

本研究针对监管文件中存在的语义复杂性问题，评估了GPT-4.0在识别监管要求中的冲突能力。通过分析设计有人工注入模糊性和矛盾的语料库，结果表明GPT-4.0在检测不一致性方面的有效性，揭示了大型语言模型在提升监管合规流程中的潜力。尽管需要进一步的大型数据集测试和领域特定的微调，但研究结果为行业实施提供了开端。

基于自然语言处理的监管合规——使用GPT 4.0解读监管文件