标题： 应该让LLMs遗忘什么？ 量化LLMs中的个人数据以满足被遗忘权请求 显示英文标题

标题： What Should LLMs Forget? Quantifying Personal Data in LLMs for Right-to-Be-Forgotten Requests

摘要： 大型语言模型（LLMs）可以记忆和泄露个人信息，引发了对符合欧盟GDPR的担忧，特别是被遗忘权（RTBF）。现有的机器遗忘方法假设要遗忘的数据已经知道，但没有解决如何识别模型中存储的个体事实关联。隐私审计技术通常在人口层面操作或针对一小部分标识符，限制了对个体层面数据查询的适用性。我们引入了WikiMem，一个包含超过5000个自然语言信标的数据集，涵盖了来自Wikidata的243个人类相关属性，并提出了一个与模型无关的度量来量化LLMs中的人类事实关联。我们的方法使用校准后的负对数似然，在改写提示中对真实值与反事实进行排序。我们在15个LLMs（410M-70B参数）上评估了200个人，结果表明记忆与主体网络存在性和模型规模相关。我们为在个体层面识别LLMs中的记忆个人数据提供了基础，使能够动态构建遗忘集用于机器遗忘和RTBF请求。 显示英文摘要

摘要： Large Language Models (LLMs) can memorize and reveal personal information, raising concerns regarding compliance with the EU's GDPR, particularly the Right to Be Forgotten (RTBF). Existing machine unlearning methods assume the data to forget is already known but do not address how to identify which individual-fact associations are stored in the model. Privacy auditing techniques typically operate at the population level or target a small set of identifiers, limiting applicability to individual-level data inquiries. We introduce WikiMem, a dataset of over 5,000 natural language canaries covering 243 human-related properties from Wikidata, and a model-agnostic metric to quantify human-fact associations in LLMs. Our approach ranks ground-truth values against counterfactuals using calibrated negative log-likelihood across paraphrased prompts. We evaluate 200 individuals across 15 LLMs (410M-70B parameters), showing that memorization correlates with subject web presence and model scale. We provide a foundation for identifying memorized personal data in LLMs at the individual level, enabling the dynamic construction of forget sets for machine unlearning and RTBF requests.