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New multilingual privacy tagger outperforms GLiNER2, Presidio, and OpenAI

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new multilingual named-entity tagger designed for privacy redaction, outperforming existing tools like GLiNER2, Microsoft Presidio, and OpenAI Privacy Filter. This tagger, fine-tuned on a multilingual encoder, achieves an 88.8 F1 score for redaction and 76.3 for exact typed-span identification across 35 languages. The system is significantly faster than local LLMs, offering 4.9 times the CPU throughput of GLiNER2, making it efficient for privacy-preserving text analysis. AI

IMPACT This new privacy tagging model offers a significant speed and accuracy improvement over existing solutions, potentially accelerating the adoption of privacy-preserving NLP techniques.

RANK_REASON The item is a research paper detailing a new model and its performance on specific benchmarks. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New multilingual privacy tagger outperforms GLiNER2, Presidio, and OpenAI

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The item is a research paper detailing a new model and its performance on specific benchmarks. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Jonathan Graehl ·

    Strong Multilingual Privacy Tagging at Encoder Speed

    arXiv:2609.38630v1 Announce Type: new Abstract: Privacy redaction must remove personal information while preserving relationships expressed in text. We develop a multilingual named-entity tagger with fine-grained distinctions supporting varied redaction policies and methods for c…

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