Exploring the Application of Large Language Models in Detecting and Protecting Personally Identifiable Information in Archival Data: A Comprehensive Study*

This comprehensive study investigates the application of Large Language Models (LLMs) for detecting and protecting Personally Identifiable Information (PII) in archival data, a pressing concern for archives under the mandate to increase public access while safeguarding personal privacy. The paper juxtaposes traditional supervised learning methods against LLMs’ unsupervised capabilities in PII detection, unveiling LLMs as viable alternatives capable of achieving satisfactory performance levels without the need for extensive training datasets. Through empirical analysis, the study validates the feasibility of LLMs in identifying sensitive information within large volumes of archival material. The findings highlight LLMs’ significant interpretability, providing understandable rationale behind PII identification—a feature that not only enhances trust in AI applications but also aids archival staff in the review process. This research contributes novel insights into the intersection of AI and archival science, presenting LLMs as powerful tools for addressing the twin challenges of data accessibility and privacy.