🤖 AI Summary
Detecting heterogeneous, context-sensitive personally identifiable information (PII) across multiple languages and scenarios faces dual challenges of scarce labeled data and privacy risks. This work proposes a lightweight solution by constructing a 0.3B-parameter model based on the GLiNER2 architecture, trained via character-level span annotation and transfer learning. The approach introduces a constraint-driven generation pipeline to synthesize 4,910 multilingual text samples, marking the first integration of constraint-driven synthetic data with an efficient named entity recognition framework to enable accurate cross-lingual extraction of 42 PII categories. Evaluated on the SPY benchmark, the method achieves significantly higher span-level F1 scores than five existing systems, including the OpenAI Privacy Filter, and the model is publicly released to advance open research in PII detection.
📝 Abstract
Reliable detection of personally identifiable information (PII) is increasingly important across modern data-processing systems, yet the task remains difficult: PII spans are heterogeneous, locale-dependent, context-sensitive, and often embedded in noisy or semi-structured documents. We present GLiNER2-PII, a small 0.3B-parameter model adapted from GLiNER2 and designed to recognize a broad taxonomy of 42 PII entity types at character-span resolution. Training such systems, however, is constrained by the scarcity of shareable annotated data and the privacy risks associated with collecting real PII at scale. To address this challenge, we construct a multilingual synthetic corpus of 4,910 annotated texts using a constraint-driven generation pipeline that produces diverse, realistic examples across languages, domains, formats, and entity distributions. On the challenging SPY benchmark, GLiNER2-PII achieves the highest span-level F1 among five compared systems, including OpenAI Privacy Filter and three GLiNER-based detectors. We publicly release the model on Hugging Face to support further research and practical deployment of open PII detection systems.