In conversational search systems, a key component is to determine and clarify the intent behind complex queries. We view intent clarification in light of the exploratory search paradigm, where users, through an iterative, evolving process of selection, exploration and retrieval, transform a visceral or conscious need into a formalized one. Augmenting the clarification component with a retrieval step (retrieval-augmented intent clarification) can seriously enhance clarification performance, especially in domains where Large Language Models (LLMs) lack parametric knowledge. However, in more sensitive domains, such as healthcare, government (e.g. FOIA search) or legal contexts, the retrieval database may contain sensitive information that needs protection. In this paper, we explore the research challenge of developing a retrieval-augmented conversational agent that can act as a mediator and gatekeeper for the sensitive collection. To do that, we also need to know what we are protecting and against what. We propose to tackle this research challenge in three steps: 1) define an attack model, 2) design sensitivity-aware defenses on the retrieval level and 3) develop evaluation methods to measure the trade-off between the level of protection and the system's utility.

翻译：在对话式搜索系统中，一个关键组件是确定并澄清复杂查询背后的意图。我们将意图澄清置于探索式搜索范式的视角下进行审视：用户通过选择、探索与检索的迭代演化过程，将内在或意识层面的需求转化为形式化的需求。通过检索步骤增强澄清组件（检索增强的意图澄清）能显著提升澄清性能，尤其在大型语言模型缺乏参数化知识的领域。然而，在医疗健康、政府（如《信息自由法》检索）或法律等更敏感的领域中，检索数据库可能包含需要保护的敏感信息。本文探讨了开发检索增强对话代理的研究挑战，该代理需能作为敏感信息集合的中介与守门人。为此，我们还需明确保护对象及防范目标。我们提出通过三个步骤应对这一研究挑战：1）定义攻击模型，2）在检索层面设计敏感性感知防御机制，3）开发评估方法以衡量保护水平与系统效用之间的权衡关系。