Domain Agnostic Text Redaction from Natural Language Rules using Instruction Tuning

• URL: http://arxiv.org/abs/2608.14693v1
• Date: Sun, 09 Aug 2026 14:20:13 GMT
• Title: Domain Agnostic Text Redaction from Natural Language Rules using Instruction Tuning
• Abstract summary: This paper introduces an explainable, domain-agnostic text redaction solution that uses natural language rules of redaction.<n>Unlike traditional text sanitization, this method enables a user to conveniently define any sensitive information.<n>The solution shows high reconstruction error and high redaction precision, making it suitable for automated text sanitization.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: With the increasing digitization of personal and corporate communication, the automatic sanitization of textual data has become a crucial component of data privacy and compliance frameworks. Traditional text sanitization solutions are majorly suitable for obscuring sensitive data with standard structure such as Personal Identifiable Information (PII). These solutions do not provide transparent justification for their redaction, which makes it difficult to audit them. This paper introduces an explainable, domain-agnostic text redaction solution that uses natural language rules of redaction, applied via an instruction-tuned language model, to identify and redact sensitive information in unstructured documents. Unlike traditional text sanitization, this method enables a user to conveniently define any sensitive information; which may be structured (e.g.\ PII) or unstructured (e.g.\ legal terms and conditions) in natural language. A general-purpose LLM generates or augments these natural language rules of redaction from the user's definition, which are then used to instruction-fine-tune a smaller language model that reasons the rules step-by-step over any given document to identify and redact the corresponding sensitive content, while providing transparent justifications for each redaction and highlighting the specific rule that triggered the decision. This explanation is generated in natural language to support human reviewers and auditors in understanding why specific content was redacted. A reconstruction-based metric is used to estimate the probability of recovering redacted information from the sanitized document, quantifying redaction coverage. The solution shows high reconstruction error and high redaction precision, making it suitable for automated text sanitization in critical applications such as legal discovery, medical documentation, and corporate information governance.

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