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LLMs accelerate Persian chat anonymization with efficient NER training

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a method for efficient anonymization of Persian customer chats using LLM-labeled data. They compared three instruction-tuned LLMs—DeepSeek-V3-0324, GPT-OSS-120B, and Qwen3-235B-A22B-Instruct-2507—to generate annotations for training a compact Named Entity Recognition (NER) model. The study found that supervision from GPT-OSS-120B in a zero-shot setting resulted in the best performance for the trained NER model, achieving high macro-F1 and Label Coverage Recall. This approach allows for rapid and cost-effective anonymization of large datasets on consumer hardware. AI

IMPACT Enables cost-effective and rapid anonymization of sensitive Persian text data for industrial applications.

RANK_REASON The cluster contains an academic paper detailing a new method for LLM-based data anonymization. [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 →

LLMs accelerate Persian chat anonymization with efficient NER training

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26 / 100
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The cluster contains an academic paper detailing a new method for LLM-based data anonymization. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Mohammad Hossein Shalchian, Mostafa Amiri, Amir Mahdi Sadeghzadeh ·

    PersianAnonymizer: Evaluating LLM-Labeled Training for Efficient NER-based Anonymization in Persian

    arXiv:2609.00958v1 Announce Type: new Abstract: We target practical anonymization of Persian customer chats by training a compact NER model from LLM-labeled supervision and selecting the best labeler for deployment. We compare three instruction-tuned LLMs: DeepSeek-V3-0324, GPT-O…

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