解释类型影响用户对RAG答案的信任，清晰可操作更重要。
Trust Me on This: A User Study of Trustworthiness for RAG Responses
- 对比三种解释：来源标注、事实依据、信息覆盖度
- 解释提升用户选优质答案率，但非唯一决定因素
- 清晰度和可操作性比客观质量更影响信任判断
生成式AI在信息检索系统中常提供缺乏透明度的合成答案。本研究探究不同解释类型如何影响用户对检索增强生成系统响应的信任。我们开展了一项受控的双阶段用户实验，参与者需从两份回答中选择更可信的一份——一份客观质量更高，另一份质量较低，两者均附带三种解释类型之一：(1) 来源标注，(2) 事实依据，(3) 信息覆盖度。结果表明，解释显著引导用户选择高质量回答，但信任不仅由客观质量决定：用户判断还受回答清晰度、可操作性及自身先验知识的显著影响。
原文摘要 · Abstract (English)
The integration of generative AI into information access systems often presents users with synthesized answers that lack transparency. This study investigates how different types of explanations can influence user trust in responses from retrieval-augmented generation systems. We conducted a controlled, two-stage user study where participants chose the more trustworthy response from a pair-one objectively higher quality than the other-both with and without one of three explanation types: (1) source attribution, (2) factual grounding, and (3) information coverage. Our results show that while explanations significantly guide users toward selecting higher quality responses, trust is not dictated by objective quality alone: Users' judgments are also heavily influenced by response clarity, actionability, and their own prior knowledge.
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