REDACT: A Systematically Controlled Multilingual Benchmark for Personal Information Detection
Accepted to the EMNLP 2026 Industry Track
Paper / arXiv · GitHub
REDACT is a multilingual benchmark for evaluating personal information detection under systematically controlled generation conditions.
13,427 records · 324,078 entity annotations · 51 canonical entity types · 25 languages · 9 scripts · 4,127 surface-form patterns
The headline task is… See the full description on the dataset page:
https://huggingface.co/datasets/guneeshv/REDACT-PII-Benchmark.
