可扩展的多语言PII标注：面向大语言模型负责任AI
Scalable multilingual PII annotation for responsible AI in LLMs
AI总结：
本文提出一个可扩展的多语言数据策展框架，通过人在回路的分阶段标注和根本原因分析，在13个低资源语言区域覆盖约336种PII类型，显著提升召回率并降低误报率，为LLM负责任AI提供高保真训练数据。
摘要：
随着大语言模型（LLMs）获得更广泛的采用，确保其在多样化的监管环境中可靠地处理个人身份信息（PII）已变得至关重要。本工作引入了一个可扩展的多语言数据策展框架，旨在针对13个代表性不足的语言区域进行高质量的PII标注，覆盖约336种特定于语言区域的PII类型。我们分阶段、人在回路中的标注方法结合了语言学专业知识与严格的质量保证，从试点、培训到生产阶段，显著提高了召回率并降低了误报率。通过利用标注者间一致性指标和根本原因分析，该框架系统地发现并解决了标注不一致问题，从而生成适用于监督式LLM微调的高保真数据集。除了报告实证改进外，我们还强调了多语言PII标注中常见的标注者挑战，并展示了迭代式、数据驱动的流程如何提升标注质量及下游模型的可靠性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
