# An Evaluation Study of Hybrid Methods for Multilingual PII Detection

arXiv: https://arxiv.org/abs/2510.07551 · HTML: https://mlsanity.com/paper/2510.07551/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 23 references that are in the map.
- First posted on arXiv 8 Oct 2025.

## Builds on (most cited first, 7 of 7)

- [GPT-NER: Named Entity Recognition via Large Language Models](https://mlsanity.com/paper/2304.10428/) (2023)
- [MultiCoNER: A Large-scale Multilingual dataset for Complex Named Entity Recognition](https://mlsanity.com/paper/2208.14536/) (2022)
- [Sketch-Driven Regular Expression Generation from Natural Language and Examples](https://mlsanity.com/paper/1908.05848/) (2019)
- [Enhancing the De-identification of Personally Identifiable Information in Educational Data](https://mlsanity.com/paper/2501.09765/) (2025)
- [Few-Shot Cross-lingual Transfer for Coarse-grained De-identification of Code-Mixed Clinical Texts](https://mlsanity.com/paper/2204.04775/) (2022)
- [Automated PII Extraction from Social Media for Raising Privacy Awareness: A Deep Transfer Learning Approach](https://mlsanity.com/paper/2111.09415/) (2021)
- [ProgGen: Generating Named Entity Recognition Datasets Step-by-step with Self-Reflexive Large Language Models](https://mlsanity.com/paper/2403.11103/) (2024)

## Built on by (most cited first, 2 of 2)

- [A Comparative Study of Light-weight Language Models for PII Masking and their Deployment for Real Conversational Texts](https://mlsanity.com/paper/2512.18608/) (2025)
- [PANOPTICON: A PII-Based Assemblage of Naturalistic Output Tokens for Investigating Privacy Leakage Within LLM Context Window](https://mlsanity.com/paper/2607.22695/) (2026)

## Abstract (arXiv)

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.