Detecting Sensitive Personal Information in Japanese Pre-Training Corpora for Large Language Models

2026-06-10 · Source: Takara TLDR - Daily AI Papers · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Cybersecurity & Data Privacy · Depth: Expert, quick

Summary

The study addresses the critical need to detect and filter sensitive personal information, specifically "special care-required personal information (SCPI)" as defined by Japan's Act on the Protection of Personal Information (APPI), within large-scale Japanese pre-training corpora for large language models (LLMs). This research is the first to explore SCPI detection in Japanese text, aiming to ensure privacy compliance and prevent unintended data leakage. Researchers constructed an SCPI dataset using LLM-based annotation techniques. Subsequently, machine learning models were trained on this dataset, resulting in an SCPI classifier capable of effectively identifying relevant information. The work highlights the unique challenges associated with achieving accurate detection in Japanese text.

Key takeaway

For NLP Engineers or AI Security Engineers developing or deploying large language models with Japanese pre-training data, you must prioritize robust detection of special care-required personal information (SCPI). Your data pipeline should integrate specialized classifiers, like the one proposed, to ensure compliance with Japan's APPI and mitigate privacy risks. Proactively filtering SCPI prevents unintended information leakage and builds trust in your models.

Key insights

This study introduces the first effective method for detecting special care-required personal information (SCPI) in Japanese LLM corpora.

Principles

• Privacy compliance requires language-specific solutions.
• LLM-based annotation can build specialized datasets.
• SCPI detection is crucial for LLM pre-training.

Method

Researchers constructed an SCPI dataset via LLM-based annotation. Machine learning models were then trained on this dataset to rapidly classify and detect SCPI within Japanese text corpora.

In practice

• Filter Japanese LLM pre-training data for SCPI.
• Apply LLM-based annotation for custom dataset creation.

Topics

• Sensitive Personal Information
• Japanese LLMs
• Data Privacy
• APPI Compliance
• Machine Learning Detection
• Corpus Filtering

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

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Editorial summary, takeaway, and curation by AIssential. Original article published by Takara TLDR - Daily AI Papers.