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arxiv: 2606.12114 · v1 · pith:HCD6DJZZnew · submitted 2026-06-10 · 💻 cs.CL

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

Rei Minamoto , Yusuke Oda , Daisuke Kawahara This is my paper

Pith reviewed 2026-06-27 09:53 UTC · model grok-4.3

classification 💻 cs.CL
keywords sensitive personal informationSCPIJapanese textLLM pre-trainingprivacy detectiontext classificationdata filteringAPPI
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The pith

A classifier trained on LLM-annotated Japanese text can detect special care-required personal information in pre-training corpora.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper builds a dataset of special care-required personal information (SCPI) instances in Japanese by using large language models for annotation, then trains standard machine learning classifiers on that data. These classifiers are shown to identify SCPI-related text effectively. The work fills a gap because prior sensitive-information detection research has focused on English and other languages, while Japanese privacy rules under the Act on the Protection of Personal Information define SCPI as a distinct category. A reader would care if the method lets builders of Japanese LLMs remove sensitive content before training, reducing leakage risk and supporting regulatory compliance.

Core claim

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.

What carries the argument

The SCPI classifier, a machine learning model trained on an LLM-annotated dataset of Japanese text labeled for special care-required personal information under Japan's APPI.

If this is right

  • Japanese LLM pre-training pipelines can insert the classifier as a filter step to remove SCPI before model training.
  • Compliance with Japan's Act on the Protection of Personal Information becomes more feasible for large-scale text collection.
  • Rapid scanning of web-scale Japanese corpora becomes practical without exhaustive manual review.
  • Future work can treat the reported classifier performance as a baseline for Japanese SCPI detection.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same LLM-annotation-plus-classifier pattern could be tested on other languages that lack mature sensitive-data detectors.
  • Error patterns in the Japanese results may point to language-specific cues that future detectors must handle explicitly.
  • Deploying the classifier inside data-cleaning pipelines could shift standard practice for any non-English LLM training effort.
  • Hybrid human review of borderline cases flagged by the model might further raise label quality beyond pure LLM annotation.

Load-bearing premise

Labels produced by the LLM annotator are accurate and unbiased enough to serve as reliable training data for the downstream SCPI detector.

What would settle it

A side-by-side human review of several hundred LLM-annotated examples that finds more than 20 percent label errors on SCPI presence or absence.

Figures

Figures reproduced from arXiv: 2606.12114 by Daisuke Kawahara, Rei Minamoto, Yusuke Oda.

Figure 1
Figure 1. Figure 1: SCPI detection pipeline In Japan, the Act on the Protection of Per￾sonal Information (APPI) (Ministry of Justice (Japan), 2023) defines these attributes as special care-required personal information (SCPI) in Arti￾cle 2. When SCPI remains in training corpora or is memorized by LLMs, the social and legal impacts of leakage will be even more severe. Therefore, corpora collected by crawling must be filtered f… view at source ↗
Figure 2
Figure 2. Figure 2: SCPI dataset construction pipeline texts to be classified and a single SCPI label as￾signed to the text. 4.1 SCPI Labels As mentioned in Section 2.2, the APPI defines 11 categories of SCPI. Descriptions of each label are provided in Appendix A. Additionally, we include non-SCPI, resulting in a total of 12 categories as labels. 4.2 Dataset Construction Method The dataset is constructed in two stages: (1) pe… view at source ↗
Figure 3
Figure 3. Figure 3: Cross-validation results for each base model on the training data [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Processing speed for full-corpus inference [PITH_FULL_IMAGE:figures/full_fig_p006_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Precision-Recall curves for base models Doc2Vec and TF-IDF were run on a single CPU core, while Ruri and ModernBERT were executed using a single GPU. Even without parallelization, classical models are approximately 4 to 50 times faster than Transformer models. Considering the cost difference between CPUs and GPUs as well as the potential for parallelization, the practical speed advantage in real-world appl… view at source ↗
Figure 6
Figure 6. Figure 6: Normalized confusion matrix for SVC [PITH_FULL_IMAGE:figures/full_fig_p009_6.png] view at source ↗
Figure 8
Figure 8. Figure 8: Manual validation flow and as “Not sensitive” if they do not contain sen￾sitive information. Detailed results are shown in [PITH_FULL_IMAGE:figures/full_fig_p013_8.png] view at source ↗
read the original abstract

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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 2 minor

Summary. The manuscript claims to be the first study on detecting special care-required personal information (SCPI) under Japan's APPI in Japanese pre-training corpora for LLMs. It constructs an SCPI dataset via LLM-based annotation, trains machine learning classifiers on this data, and asserts that the resulting SCPI classifier can effectively identify SCPI instances.

Significance. If the effectiveness claim were supported by rigorous validation, the work would fill a genuine gap in privacy-preserving data curation for Japanese LLMs and offer a practical detection tool. The absence of any quantitative evidence for either label quality or classifier performance, however, prevents assessment of whether this contribution is realized.

major comments (3)
  1. [Abstract] Abstract: the central claim that 'our SCPI classifier can effectively identify information related to SCPI' is stated without any reported precision, recall, F1, accuracy, baseline comparisons, or error analysis. This directly undermines evaluation of the paper's primary result.
  2. [Methods / Dataset construction] The construction of the training labels relies on LLM-based annotation, yet no human validation, inter-annotator agreement, or bias audit is described. Because SCPI definitions involve culturally specific phrasing under APPI, unvalidated LLM labels constitute a load-bearing risk to all downstream claims.
  3. [Results / Experiments] No section or table presents classifier performance metrics or ablation studies. Without these, the assertion of effective detection cannot be distinguished from an untested pipeline.
minor comments (2)
  1. [Introduction] Clarify the exact definition of SCPI used for annotation and whether it follows the APPI statutory language verbatim.
  2. [Methods] Provide the full list of LLM prompts and any post-processing rules applied during annotation.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive comments identifying gaps in quantitative support and validation. We agree these elements are necessary to substantiate the claims and will perform a major revision to add the missing details.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the central claim that 'our SCPI classifier can effectively identify information related to SCPI' is stated without any reported precision, recall, F1, accuracy, baseline comparisons, or error analysis. This directly undermines evaluation of the paper's primary result.

    Authors: We agree that the abstract claim requires supporting metrics. In the revised manuscript we will update the abstract to report precision, recall, F1, accuracy, baseline comparisons, and a concise error analysis so readers can evaluate the primary result. revision: yes

  2. Referee: [Methods / Dataset construction] The construction of the training labels relies on LLM-based annotation, yet no human validation, inter-annotator agreement, or bias audit is described. Because SCPI definitions involve culturally specific phrasing under APPI, unvalidated LLM labels constitute a load-bearing risk to all downstream claims.

    Authors: We accept that LLM annotation requires validation given the cultural and legal specificity of SCPI. The revision will add human validation on a sample, inter-annotator agreement statistics, and a bias audit in the methods section. revision: yes

  3. Referee: [Results / Experiments] No section or table presents classifier performance metrics or ablation studies. Without these, the assertion of effective detection cannot be distinguished from an untested pipeline.

    Authors: We acknowledge the results section lacks the required metrics and ablations. The revised manuscript will add a results section containing tables with performance metrics (precision, recall, F1, accuracy), baseline comparisons, and ablation studies. revision: yes

Circularity Check

0 steps flagged

Standard empirical ML pipeline with no definitional or self-referential reduction

full rationale

The paper constructs an SCPI dataset via LLM-based annotation then trains standard ML classifiers on that dataset. This matches the reader's assessment of a conventional annotation-plus-supervised-training workflow. No equations, fitted parameters renamed as predictions, self-citation chains, or uniqueness theorems appear in the provided abstract or description. The central claim ('our SCPI classifier can effectively identify information related to SCPI') is presented as the outcome of ordinary training and evaluation rather than any reduction to its own inputs by construction. No load-bearing step matches any of the enumerated circularity patterns.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract supplies no explicit free parameters, axioms, or invented entities.

pith-pipeline@v0.9.1-grok · 5664 in / 1026 out tokens · 20692 ms · 2026-06-27T09:53:45.008391+00:00 · methodology

discussion (0)

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Reference graph

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