AI幻觉的信号：为VR中的具身对话智能体设计幻觉感知线索
Signals of AI Hallucination: Designing Hallucination-Aware Cues for Embodied Conversational Agents in VR
- North Carolina State University（北卡罗来纳州立大学）
- Carnegie Mellon University（卡内基梅隆大学）
- Xi’an Jiaotong-Liverpool University（西交利物浦大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本研究通过用户实验比较VR中具身对话智能体的三种幻觉感知线索设计，发现具身线索提升信任与沉浸，文本线索解释性最强，为幻觉提示界面提供设计参考。
AI中文摘要：
由大语言模型驱动的对话智能体（CAs）通常会在其回应中呈现不确定性和来源线索，以帮助用户评估回应的可靠性并识别潜在的幻觉。在虚拟现实（VR）等沉浸式环境中，对话智能体通常采用基于语音的具身对话智能体（ECAs）形式，此时不确定性和来源线索无法依赖持续的内联文本，且通过语音传递时可能被错过或干扰理解。我们进行了一项受试者内研究（N=24），比较了在VR中呈现幻觉感知信息（不确定性和来源）的三种设计，并与无线索基线进行对比：使用手势和姿态的具身线索、使用视觉指示器的图标线索，以及使用带内联引用的彩色编码文本的文本线索。我们评估了这些设计如何影响用户识别幻觉相关信息的能力、对ECA的信任度以及交互体验（沉浸感和任务负荷）。结果表明，所有三种设计都能帮助用户识别幻觉。具身线索与更高的信任度和沉浸感相关，文本线索提供了更清晰的解释性，而图标线索在保持相对较好的解释性的同时，与具身线索和文本线索相比，对沉浸感的干扰较小。这项工作通过比较沉浸式ECA环境中幻觉线索的不同设计，并考察它们如何影响用户识别幻觉的能力和体验，为VR和AI研究社区做出了贡献。它还为开发未来的ECA幻觉感知界面提供了实用的见解和设计启示。
英文摘要：
LLM-powered conversational agents (CAs) often present uncertainty and provenance cues alongside their responses to help users assess response reliability and identify potential hallucinations. In immersive environments such as Virtual Reality (VR), CAs often take the form of speech-based embodied conversational agents (ECAs), where uncertainty and provenance cues cannot rely on persistent inline text and may be missed or disrupt comprehension when delivered through speech. We conducted a within-subjects study (N = 24) to compare three designs for presenting the hallucination-awareness information (uncertainty and provenance) in ECAs in VR against a no-cue baseline: embodied cues using gestures and posture, icon cues using visual indicators, and text cues using color-coded text with inline citations. We evaluated how these designs affect users' ability to identify hallucination-related information, trust in the ECA, and interaction experience (immersion and task load). Our results show that all three designs support users in identifying hallucinations. Embodied cues were associated with higher trust and immersion, text cues offered clearer interpretability, and icon cues preserved relatively good interpretability while causing less disruption to immersion compared with embodied cues and text cues. This work contributes to the VR and AI research community by comparing different designs of hallucination cues in immersive ECA settings and examining how they affect users' ability and experiences to identify hallucinations. It also offers practical insights and design implications for developing future hallucination-awareness interfaces for ECA.
