用GPT-4.0自动识别监管文件中的矛盾，提升合规效率
NLP-based Regulatory Compliance -- Using GPT 4.0 to Decode Regulatory Documents
- 通过注入人为矛盾的语料库测试GPT-4.0的冲突检测能力
- 在验证中达到高精度、高召回率，专家认可结果可靠性
- 适合法律科技、金融合规等领域从业者参考
大型语言模型（如GPT-4.0）在应对监管文件的语义复杂性方面展现出显著潜力，尤其在识别要求之间的不一致与矛盾方面。本研究通过与架构师及合规工程师协作构建的语料库，对GPT-4.0检测监管要求内部冲突的能力进行评估，该语料库包含人工注入的模糊性与矛盾。采用精确率、召回率和F1分数等指标，实验表明GPT-4.0在检测不一致方面表现有效，结果经由人类专家验证。研究凸显了大模型在提升合规流程中的潜力，但需进一步在更大规模数据集上测试，并通过领域特定微调以提高准确性和实际应用价值。未来工作将探索自动化冲突解决及与产业伙伴合作开展试点项目。
原文摘要 · Abstract (English)
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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