构建多语言敏感信息标注框架，提升大模型在13个语种中的隐私保护能力
Scalable multilingual PII annotation for responsible AI in LLMs
- 分阶段人机协同标注，结合语言专家与质量控制
- 覆盖336类本地化敏感信息，召回率与误报率显著改善
- 适合关注AI隐私合规与多语言数据治理的研究者
随着大语言模型广泛应用，确保其在多元监管环境下对个人身份信息（PII）的可靠处理变得至关重要。本文提出一种可扩展的多语言数据整理框架，针对13个资源匮乏语境进行高质量PII标注，涵盖约336种本地化敏感信息类型。采用分阶段、人机协同的标注方法，融合语言学专业知识与严格的质量保障机制，使召回率和误报率在试点、训练与生产阶段均显著提升。通过标注者一致性指标与根因分析，系统识别并解决标注不一致问题，生成适用于监督微调的高保真数据集。除报告实证收益外，本文还揭示多语言PII标注中的常见挑战，并展示迭代式、数据驱动的流程如何提升标注质量与下游模型可靠性。
原文摘要 · Abstract (English)
As Large Language Models (LLMs) gain wider adoption, ensuring their reliable handling of Personally Identifiable Information (PII) across diverse regulatory contexts has become essential. This work introduces a scalable multilingual data curation framework designed for high-quality PII annotation across 13 underrepresented locales, covering approximately 336 locale-specific PII types. Our phased, human-in-the-loop annotation methodology combines linguistic expertise with rigorous quality assurance, leading to substantial improvements in recall and false positive rates from pilot, training, and production phases. By leveraging inter-annotator agreement metrics and root-cause analysis, the framework systematically uncovers and resolves annotation inconsistencies, resulting in high-fidelity datasets suitable for supervised LLM fine-tuning. Beyond reporting empirical gains, we highlight common annotator challenges in multilingual PII labeling and demonstrate how iterative, analytics-driven pipelines can enhance both annotation quality and downstream model reliability.
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