Sensitivity-Aware Retrieval-Augmented Intent Clarification

TL;DR

This paper proposes a sensitivity-aware retrieval-augmented approach for intent clarification in conversational search, focusing on protecting sensitive data while maintaining system utility.

Contribution

It introduces a framework for developing defenses in retrieval-augmented systems that handle sensitive information, including attack models and evaluation methods.

Findings

Proposed a three-step approach for sensitivity-aware defenses.

Developed evaluation methods for protection-utility trade-offs.

Outlined a new research direction for secure conversational agents.

Abstract

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.