波斯语数据匿名化新方案，用LLM标注训练轻量NER模型，消费级GPU上2分钟处理4万消息。
研究人员使用DeepSeek-V3-0324、GPT-OSS-120B和Qwen3-235B-A22B-Instruct-2507三种LLM为波斯语聊天数据生成标注，训练了四个MatinaRoberta-based NER模型。OSS_ZeroShot标注产生的模型在宏平均F1和标签覆盖率召回率(LCR)上表现最佳，能在单块RTX 3090上约2分钟内完成4万条消息的匿名化处理。
PersianAnonymizer: Evaluating LLM-Labeled Training for Efficient NER-based Anonymization in Persian
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-OSS-120B, and Qwen3-235B-A22B-Instruct-2507, to produce span annotations under a shared JSON protocol, yielding four corpora (OSS_ZeroShot, Qwen_ZeroShot, Qwen_FewShot, DeepSeek_FewShot). A MatinaRoberta-based token-classifier is trained per corpus and evaluated with token-level Precision/Recall/F1 (overall and per-class). We also report Label Coverage Recall (LCR), the proportion of gold non-O tokens predicted as non-O, and quantify cross-labeler behavior via a token-level Venn on test annotations. Finally, we contrast test-set annotation latency of the LLMs on H200 nodes with the trained NER's test-time labeling on a single RTX 3090. Results show that supervision from OSS_ZeroShot yields the strongest macro-F1 and LCR, while the resulting NER labels an entire 40K-message test set in approximately 2 minutes on one consumer GPU. This establishes a practical path to high-quality, low-cost anonymization for Persian industrial data.