标题： PII-Compass：通过接地引导LLM训练数据提取提示朝着目标PII 显示英文标题

标题： PII-Compass: Guiding LLM training data extraction prompts towards the target PII via grounding

摘要： 最新且最具影响力的大型模型进展源于其规模的增加。 不幸的是，这转化为更强的记忆能力，引发了数据隐私问题。 具体来说，已经证明模型可以输出其训练数据中包含的个人可识别信息（PII）。 然而，报告的PII提取性能差异很大，对于评估此风险的最佳方法尚无共识，导致低估了现实中的对手。 在本工作中，我们实证表明，通过使用领域内数据对人工构建的提取提示的前缀进行定位，可以将PII的可提取性提高十倍以上。 我们的方法PII-Compass，在1、128和2308次查询的情况下，分别实现了0.92%、3.9%和6.86%的电话号码提取率，即每15人中就有1人的电话号码可以被提取。 显示英文摘要

摘要： The latest and most impactful advances in large models stem from their increased size. Unfortunately, this translates into an improved memorization capacity, raising data privacy concerns. Specifically, it has been shown that models can output personal identifiable information (PII) contained in their training data. However, reported PIII extraction performance varies widely, and there is no consensus on the optimal methodology to evaluate this risk, resulting in underestimating realistic adversaries. In this work, we empirically demonstrate that it is possible to improve the extractability of PII by over ten-fold by grounding the prefix of the manually constructed extraction prompt with in-domain data. Our approach, PII-Compass, achieves phone number extraction rates of 0.92%, 3.9%, and 6.86% with 1, 128, and 2308 queries, respectively, i.e., the phone number of 1 person in 15 is extractable.