An Evaluation Study of Hybrid Methods for Multilingual PII Detection

• URL: http://arxiv.org/abs/2510.07551v1
• Date: Wed, 08 Oct 2025 21:03:59 GMT
• Title: An Evaluation Study of Hybrid Methods for Multilingual PII Detection
• Authors: Harshit Rajgarhia, Suryam Gupta, Asif Shaik, Gulipalli Praveen Kumar, Y Santhoshraj, Sanka Nithya Tanvy Nishitha, Abhishek Mukherji,
• Abstract summary: We present RECAP, a framework that combines deterministic regular expressions with context-aware large language models (LLMs) for scalable PII detection.<n>Our system outperforms fine-tuned NER models by 82% and zero-shot LLMs by 17% in weighted F1-score.<n>This work offers a scalable and adaptable solution for efficient PII detection in compliance-focused applications.
• Score: 0.026059379504241156
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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