The advancement of large language models (LLMs) brings notable improvements
across various applications, while simultaneously raising concerns about
potential private data exposure. One notable capability of LLMs is their
ability to form associations between different pieces of information, but this
raises concerns when it comes to personally identifiable information (PII).
This paper delves into the association capabilities of language models, aiming
to uncover the factors that influence their proficiency in associating
information. Our study reveals that as models scale up, their capacity to
associate entities/information intensifies, particularly when target pairs
demonstrate shorter co-occurrence distances or higher co-occurrence
frequencies. However, there is a distinct performance gap when associating
commonsense knowledge versus PII, with the latter showing lower accuracy.
Despite the proportion of accurately predicted PII being relatively small, LLMs
still demonstrate the capability to predict specific instances of email
addresses and phone numbers when provided with appropriate prompts. These
findings underscore the potential risk to PII confidentiality posed by the
evolving capabilities of LLMs, especially as they continue to expand in scale
and power.

该研究探索了大型语言模型的关联能力，揭示了影响其关联信息能力的因素。研究发现随着模型规模的扩大，它们关联实体 / 信息的能力增强，但在关联常识知识与 PII 方面存在着明显的差距。这些发现强调了 LLM 对 PII 机密性的潜在威胁，特别是随着它们继续扩大规模和能力的过程中。