Strong Multilingual Privacy Tagging at Encoder Speed
Privacy redaction must reliably remove personal information while preserving the relationships expressed in the text. We develop a general-purpose multilingual named-entity tagger with distinctions fine-grained enough to support a broad range of redaction policies, and methods for cheaply learning additional distinctions. We fine-tune the multilingual encoder XLM-R-large on text in 35 languages annotated by prompted frontier language models, plus existing human-annotated corpora with labels mapped onto our entity types. We evaluate on human-annotated test sets in seven languages and on LLM-annotated text in 35 languages. On the human-annotated sets, at operating points fixed on developmen...
| Authors | Jonathan Graehl |
|---|---|
| Primary category | cs.CL |
| Published | 29 September 2026 |
| Last updated at source | 13 October 2026 |
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