Retrieval-based language models (LMs) have demonstrated improved
interpretability, factuality, and adaptability compared to their parametric
counterparts, by incorporating retrieved text from external datastores. While
it is well known that parametric models are prone to leaking private data, it
remains unclear how the addition of a retrieval datastore impacts model
privacy. In this work, we present the first study of privacy risks in
retrieval-based LMs, particularly $k$NN-LMs. Our goal is to explore the optimal
design and training procedure in domains where privacy is of concern, aiming to
strike a balance between utility and privacy. Crucially, we find that $k$NN-LMs
are more susceptible to leaking private information from their private
datastore than parametric models. We further explore mitigations of privacy
risks. When privacy information is targeted and readily detected in the text,
we find that a simple sanitization step would completely eliminate the risks,
while decoupling query and key encoders achieves an even better utility-privacy
trade-off. Otherwise, we consider strategies of mixing public and private data
in both datastore and encoder training. While these methods offer modest
improvements, they leave considerable room for future work. Together, our
findings provide insights for practitioners to better understand and mitigate
privacy risks in retrieval-based LMs. Our code is available at:
this https URL .

本文研究检测基于 k 最近邻的检索型语言模型对隐私的风险，探索隐私安全的最佳设计和培训程序以在效用和隐私之间取得平衡，并提供了隐私风险缓解策略。