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:现有的问答研究侧重于针对无法回答的问题以及总是在系统可以的情况下提供答案。但是,在系统不应该回答问题的情况下怎么办呢?这可以是为了保护敏感用户或敏感信息。许多模型在面对对手用户的审问时会暴露敏感信息。我们试图确定是否可能教授问答系统保守特定的秘密。我们设计并实现了一个概念验证架构,并通过我们的评估确定,虽然可能,但未来研究的方向有很多,以减少系统偏执(假阳性),信息泄漏(假阴性)和将实现工作扩展到保留秘密存在信息聚合的更复杂问题。