这个基准提供了具体的数据和评估，能帮你了解不同检测器在处理多语言PII时的真实表现差异。
REDACT 是一个包含13,427条记录、324,078个实体标注、51种实体类型和4,127种表面形式模式的多语言PII检测基准，覆盖25种语言的9种文字系统。该基准通过强度2覆盖阵列采样器控制9个生成轴，包括领域、格式、难度等。评估了五个检测器（Presidio、GLiNER、OpenAI Privacy Filter、GPT-4.1、Claude Sonnet 4.6）在1,000条记录上的表现，发现基于规则的检测器在高风险数据上表现较差（HIGH敏感类别召回率0.07），而LLM检测器更鲁棒。该基准还提供了实体级元数据（披露状态、披露形式、GDPR敏感等级）以支持分层评估。
REDACT: A Systematically Controlled Multilingual Benchmark for Personal Information Detection
Benchmark infrastructure for personally identifiable information (PII) detection remains limited: existing corpora cover few entity types, use ad hoc generation conditions, and do not show which surface conditions cause detector failures. We present REDACT, a systematically controlled multilingual PII benchmark with 13,427 records, 324,078 entity annotations, 51 entity types, 4,127 surface-form patterns, and 25 languages across 9 scripts. A strength-2 covering-array sampler controls nine generation axes: domain, format, difficulty, length, density, code-switching, language, adjacency, and co-occurrence. Three entity-level metadata fields (disclosure status, disclosure form, and a GDPR-aligned sensitivity tier) enable stratified evaluation beyond aggregate or per-type F1. From the full benchmark, we evaluate five detectors (Presidio, GLiNER, the OpenAI Privacy Filter, GPT-4.1, and Claude Sonnet 4.6) on a locked, language-stratified sample of 1,000 records. Aggregate F1 masks an architecture-dependent failure structure: the rule-based detector performs poorly on the highest-stakes data, including HIGH-sensitivity categories (recall 0.07) and non-verbatim disclosure forms, while the LLM detectors remain more robust, with the HIGH tier as their strongest sensitivity slice. A three-model reference-free LLM-as-judge assessment corroborates that sensitivity-tier assignment is the task's hardest axis. We release the benchmark, schema, prompts, and stratified evaluation harness.