The increasing reliance on digital information necessitates advancements in conversational search systems, particularly in terms of information transparency. While prior research in conversational information-seeking has concentrated on improving retrieval techniques, the challenge remains in generating responses useful from a user perspective. This study explores different methods of explaining the responses, hypothesizing that transparency about the source of the information, system confidence, and limitations can enhance users' ability to objectively assess the response. By exploring transparency across explanation type, quality, and presentation mode, this research aims to bridge the gap between system-generated responses and responses verifiable by the user. We design a user study to answer questions concerning the impact of (1) the quality of explanations enhancing the response on its usefulness and (2) ways of presenting explanations to users. The analysis of the collected data reveals lower user ratings for noisy explanations, although these scores seem insensitive to the quality of the response. Inconclusive results on the explanations presentation format suggest that it may not be a critical factor in this setting.

翻译：数字信息的日益依赖推动了对话式检索系统的发展，尤其在信息透明度方面。尽管先前关于对话式信息检索的研究集中于改进检索技术，但如何从用户视角生成有用响应仍是挑战。本研究探索了响应解释的不同方法，假设关于信息来源、系统置信度及局限性的透明度能够提升用户客观评估响应的能力。通过考察解释类型、质量及呈现模式三个维度的透明度，本研究旨在弥合系统生成响应与用户可验证响应之间的鸿沟。我们设计了一项用户实验，旨在回答以下问题：(1)增强响应解释质量对其有用性的影响；(2)向用户呈现解释的方式。数据分析显示，含噪声解释的用户评分较低，尽管这些评分似乎对响应质量不敏感。关于解释呈现方式的不确定性结果表明，在该场景中这可能并非关键因素。