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        <title>Training Lightweight PII Text Detectors via Rubric-Guided Contrastive Data Synthesis</title>
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    <abstract>The widespread use of large language models (LLMs) has increased the risk of leaking personally identifiable information (PII), making PII detection increasingly important. PII detection must process large text volumes efficiently, but labeled PII text is difficult to collect, share, and redistribute for privacy reasons. We aim to develop rubric-guided contrastive data synthesis for training lightweight PII text detectors from a few labeled seeds and to determine, through comparative experiments on PII benchmark datasets, whether the proposed method improves PII text detector classification performance. From about ten labeled examples, our method induces explicit criteria for distinguishing the two labels (rubrics), generates contrastive Sensitive/Non-sensitive sentence pairs, verifies and deduplicates them, and trains RoBERTa. Experiments on SPeDaC1 and SPY with Qwen3.5-9B and Gemma-4-26B-A4B showed that our method outperformed four baselines in all four settings and significantly improved Macro F1 over the Self-Instruct-style baseline in three of four settings. Overall, we developed rubric-guided contrastive data synthesis for training lightweight PII text detectors from a few labeled seeds and confirmed through comparative experiments on PII benchmark datasets that the proposed method improves classification performance. Our code is available at https://github.com/ekunish/emnlp2026-code.</abstract>
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%0 Conference Proceedings
%T Training Lightweight PII Text Detectors via Rubric-Guided Contrastive Data Synthesis
%A Ekuni, Shota
%A Zeng, Jie
%A Kobashi, Yohei
%A Kojima, Takeshi
%A Iwasawa, Yusuke
%A Matsuo, Yutaka
%Y Atanasova, Pepa
%Y Haffari, Gholamreza
%Y Wang, Minghan
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing: Industry Track
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-460-6
%F ekuni-etal-2026-training
%X The widespread use of large language models (LLMs) has increased the risk of leaking personally identifiable information (PII), making PII detection increasingly important. PII detection must process large text volumes efficiently, but labeled PII text is difficult to collect, share, and redistribute for privacy reasons. We aim to develop rubric-guided contrastive data synthesis for training lightweight PII text detectors from a few labeled seeds and to determine, through comparative experiments on PII benchmark datasets, whether the proposed method improves PII text detector classification performance. From about ten labeled examples, our method induces explicit criteria for distinguishing the two labels (rubrics), generates contrastive Sensitive/Non-sensitive sentence pairs, verifies and deduplicates them, and trains RoBERTa. Experiments on SPeDaC1 and SPY with Qwen3.5-9B and Gemma-4-26B-A4B showed that our method outperformed four baselines in all four settings and significantly improved Macro F1 over the Self-Instruct-style baseline in three of four settings. Overall, we developed rubric-guided contrastive data synthesis for training lightweight PII text detectors from a few labeled seeds and confirmed through comparative experiments on PII benchmark datasets that the proposed method improves classification performance. Our code is available at https://github.com/ekunish/emnlp2026-code.
%U https://aclanthology.org/2026.emnlp-industry.242/
%P 4090-4104

Markdown (Informal)

[Training Lightweight PII Text Detectors via Rubric-Guided Contrastive Data Synthesis](https://aclanthology.org/2026.emnlp-industry.242/) (Ekuni et al., EMNLP 2026)

• Training Lightweight PII Text Detectors via Rubric-Guided Contrastive Data Synthesis (Ekuni et al., EMNLP 2026)

ACL

• Shota Ekuni, Jie Zeng, Yohei Kobashi, Takeshi Kojima, Yusuke Iwasawa, and Yutaka Matsuo. 2026. Training Lightweight PII Text Detectors via Rubric-Guided Contrastive Data Synthesis. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing: Industry Track, pages 4090–4104, Budapest, Hungary. Association for Computational Linguistics.