RedactionBench

2026-06-18 · Source: cs.CL updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Cybersecurity & Data Privacy, Data Science & Analytics · Depth: Expert, quick

Summary

RedactionBench is a new, manually annotated benchmark designed to evaluate Personally Identifiable Information (PII) redaction in Large Language Models, addressing the critical distinction between simple entity extraction and context-dependent privacy semantics. Comprising 200 diverse documents across 11 real-world domains, the benchmark aims to overcome limitations of existing evaluation methods. Alongside RedactionBench, the authors introduce R-Score, a novel character-level metric that normalizes for semantic similarity and formatting variations in redactions. Evaluations of 35 models, including Named Entity Recognition models, Small Language Models, and frontier models, reveal that contextual redaction remains an unsolved challenge. A human evaluation involving over 80 users on RedactionBench further underscores the subjective nature of privacy, showing high consensus for mandatory redactions (89.4%) and safe text preservation (94.1%), but only 47.7% agreement for contextual redactions. The benchmark and metric are released to foster improved privacy-preserving systems.

Key takeaway

For NLP Engineers and AI Security Engineers developing or deploying LLMs in sensitive domains, recognize that PII redaction is not merely entity extraction. Your models must account for contextual privacy, which human evaluators often disagree on. Utilize RedactionBench and its R-Score metric to rigorously evaluate your systems' ability to handle nuanced, context-dependent redactions, moving beyond simple PII detection to address true privacy semantics.

Key insights

Contextual PII redaction is an unsolved problem due to subjective privacy perceptions, requiring specialized benchmarks and metrics.

Principles

• PII redaction requires contextual understanding.
• Privacy perception is highly subjective.
• Semantic similarity matters in redaction metrics.

Method

RedactionBench involves manually annotating 200 documents across 11 domains for contextual PII. R-Score is a character-level metric treating semantically similar redactions equally, nullifying shallow formatting.

In practice

• Use RedactionBench for PII redaction evaluation.
• Employ R-Score for nuanced redaction scoring.
• Account for subjective privacy in design.

Topics

• PII Redaction
• Large Language Models
• Contextual Privacy
• RedactionBench
• R-Score Metric
• Named Entity Recognition

Best for: Research Scientist, CTO, VP of Engineering/Data, AI Scientist, NLP Engineer, AI Security Engineer

Related on AIssential

• RedactionBench — Contextual PII redaction is a complex, unsolved problem fundamentally different from simple entity recognition due to subjective privacy perceptions.
• Introducing OpenAI Privacy Filter — OpenAI's Privacy Filter offers context-aware PII detection and redaction via a small, efficient, open-weight model.
• POLAR-Bench: A Diagnostic Benchmark for Privacy-Utility Trade-offs in LLM Agents — POLAR-Bench diagnoses LLM agent privacy-utility trade-offs, showing frontier models significantly outperform smaller, on-device counterparts.
• The Day I Realized Our LLM Was Quietly Logging Patient SSNs — Data protection for LLMs is an architectural problem requiring a robust redaction gate, not just instruction.
• OpenAI Just Released a Privacy Filter. Here’s What It Can’t Do — One-way PII redaction is insufficient for production LLM pipelines requiring data coherence and reversibility.
• It Takes Two: Complementary Self-Distillation for Contextual Integrity in LLMs — SelfCI decouples privacy and utility in LLMs using complementary self-distillation to achieve Contextual Integrity, outperforming RL baselines.

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CL updates on arXiv.org.