Problem
Research questions and friction points this paper is trying to address.
Personally Identifiable Information
PII
Unstructured Text
Privacy Filter
Sensitive Information
Model Card for OpenAI Privacy Filter
🤖 AI Summary
本文介绍了一种用于检测并删除非结构化文本中个人身份信息和秘密的双向标记分类模型，采用预训练转换及Viterbi解码器方法。
📝 Abstract
OpenAI Privacy Filter is a compact, bidirectional token-classification model for detecting and redacting personally identifiable information (PII) and secrets in unstructured text. The model is derived from an autoregressively pretrained checkpoint and converted into a bidirectional, banded-attention classifier that labels an input sequence in a single forward pass. A constrained Viterbi decoder produces coherent spans across eight privacy categories and exposes configurable operating points for precision-recall tradeoffs. Privacy Filter has 1.5 billion total parameters, 50 million active parameters per token, and a 128,000-token context window. It is designed for efficient local deployment and domain-specific fine-tuning. Privacy Filter is intended as a configurable data-minimization component within layered privacy workflows, not as an anonymization or compliance guarantee.
Innovation
Methods, ideas, or system contributions that make the work stand out.
bidirectional token-classification model
banded-attention classifier
constrained Viterbi decoder
configurable operating points
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