mlsanityData to 14 Oct 2026

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

arXiv 2606.12114 ↗First posted 10 Jun 2026cs.CLcited by 0

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Abstract (arXiv)

Sensitive personal information can appear in large-scale pre-training corpora for large language models (LLMs). Detecting and filtering such information is therefore essential to ensure compliance with privacy regulations and prevent unintended information leakage. However, in contrast to English and other languages, research into sensitive personal information has been limited in the Japanese language. In this study, we focus on sensitive personal data defined as special care-required personal information (SCPI) under Japan's Act on the Protection of Personal Information (APPI). We construct an SCPI dataset using LLM-based annotation and train machine learning models to rapidly detect SCPI in text. As a result, our SCPI classifier can effectively identify information related to SCPI. This study is the first to explore SCPI detection in Japanese text corpora, highlighting the challenges of accurate detection.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 8 of its 38 references that are in the map. First posted on arXiv 10 Jun 2026.

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References · 8

  1. 2017Attention Is All You Needcited by 60,361
  2. 2019Optuna: A Next-generation Hyperparameter Optimization Frameworkcited by 2,501
  3. 2025gpt-oss-120b & gpt-oss-20b Model Cardcited by 1,386
  4. 2024Dolma: an Open Corpus of Three Trillion Tokens for Language Model Pretraining Researchcited by 483
  5. 2022Are Large Pre-Trained Language Models Leaking Your Personal Information?cited by 200
  6. 2024Continual Pre-Training for Cross-Lingual LLM Adaptation: Enhancing Japanese Language Capabilitiescited by 111
  7. 2025Common Corpus: The Largest Collection of Ethical Data for LLM Pre-Trainingcited by 20
  8. 2022Is Your Model Sensitive? SPeDaC: A New Benchmark for Detecting and Classifying Sensitive Personal Datacited by 3

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