本文提出了一种名为RECAP的混合框架，结合确定性正则表达式与上下文感知的大型语言模型，以实现13种低资源语言的可扩展PII检测。该框架设计灵活，支持超过300种实体类型，无需重新训练。通过三阶段细化流程，实现了歧义消除和过滤。在nervaluate基准测试中，RECAP在加权F1分数上优于微调的NER模型82%和零样本LLM 17%，为隐私合规应用的PII检测提供了一种可扩展且适应性强的解决方案。

arXiv:2510.07551v1 Announce Type: new Abstract: The detection of Personally Identifiable Information (PII) is critical for privacy compliance but remains challenging in low-resource languages due to linguistic diversity and limited annotated data. We present RECAP, a hybrid framework that combines deterministic regular expressions with context-aware large language models (LLMs) for scalable PII detection across 13 low-resource locales. RECAP's modular design supports over 300 entity types without retraining, using a three-phase refinement pipeline for disambiguation and filtering. Benchmarked with nervaluate, our system outperforms fine-tuned NER models by 82% and zero-shot LLMs by 17% in weighted F1-score. This work offers a scalable and adaptable solution for efficient PII detection in compliance-focused applications.