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Research questionHow can PII detectors avoid missed entities when deployment data shifts from benchmark conditions?PII detectors can miss sensitive entities when real-world inputs differ from the data used for evaluation. These shifts expose different weaknesses in entity boundaries, formatting, and classification across detection approaches.
AI
Evaluation & Benchmarks
Machine Learning
Natural Language Processing
Latest papersRecent research connected to this question, newest first.Mind the Gap: Robustness Risks in PII Detection SystemsThe evidence covers a stress-test benchmark spanning seven natural distribution-shift categories and representative encoder-based NER, rule-based hybrid, and generative LLM systems. It reports degradation and distinct failure modes, and proposes a hybrid detection pipeline with QA-driven feedback; it does not establish that any architecture is uniformly reliable.research paper · Sep 3, 2026
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