English(EN) Cross-Domain Data Selection and Augmentation for Automatic Compliance Detection

研究人员开发数据选择方法以改进跨法规的AI合规性检测

作者 PulseAugur 编辑部 · [1 个来源] · 2026-04-23 09:28

研究人员开发了一种新方法，以提高自动化合规性检测系统的准确性。该研究侧重于跨领域数据选择和增强，解决了在一种法规上训练的模型在其他法规上表现不佳的挑战。通过采用随机抽样、交叉熵差异、重要性加权和基于嵌入的检索等策略，该团队证明了有针对性的数据选择可显著减少负迁移，为跨不同法律文本实现更可靠和可扩展的合规性自动化铺平了道路。 AI

影响提高了NLP模型的跨领域泛化能力，可能增强了自动法律合规工具。

排序理由详细介绍NLP任务新方法的学术论文。

在 arXiv cs.CL 阅读 →

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arXiv cs.CL TIER_1 English(EN) · Dusica Marijan · 2026-04-23 09:28

跨领域数据选择与增强用于自动合规性检测

Automating the detection of regulatory compliance remains a challenging task due to the complexity and variability of legal texts. Models trained on one regulation often fail to generalise to others. This limitation underscores the need for principled methods to improve cross-dom…

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跨领域数据选择与增强用于自动合规性检测

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