Explainable Domain-Agnostic Text Redaction via Instruction-Tuned Language Models

A new research paper on arXiv (2608.14693) introduces an explainable, domain-agnostic text redaction method that uses natural language rules applied via an instruction-tuned language model. The approach allows users to define sensitive information in natural language, covering both structured data like Personal Identifiable Information (PII) and unstructured content such as legal terms. Unlike traditional sanitization, it provides transparent justification for redactions, enhancing auditability. The method addresses the limitations of existing solutions that focus on standard structures and lack explainability. The paper is authored by researchers and was announced as a cross-type submission. The work is significant for data privacy and compliance in digitized communication, offering a flexible and auditable alternative to current redaction tools.