Existing question-answering research focuses on unanswerable questions in the context of always providing an answer when a system can\dots but what about cases where a system {\bf should not} answer a question. This can either be to protect sensitive users or sensitive information. Many models expose sensitive information under interrogation by an adversarial user. We seek to determine if it is possible to teach a question-answering system to keep a specific fact secret. We design and implement a proof-of-concept architecture and through our evaluation determine that while possible, there are numerous directions for future research to reduce system paranoia (false positives), information leakage (false negatives) and extend the implementation of the work to more complex problems with preserving secrecy in the presence of information aggregation.

翻译：现有问答研究主要关注系统在可提供答案时处理不可回答问题的场景，但忽略了系统**不应**回答问题的情形。这类情形可能涉及保护敏感用户或敏感信息。许多模型会在对抗性用户质问下暴露敏感信息。本研究旨在探究能否训练问答系统对特定事实保持机密。我们设计并实现了一套概念验证架构，通过评估验证了其可行性，同时指出未来研究需在降低系统过度谨慎（误报）、减少信息泄露（漏报）以及将保密机制扩展至更复杂的信息聚合场景等方面展开深入探索。