具身与否：具身对基于大语言模型的对话智能体用户感知的影响
To Embody or Not: The Effect Of Embodiment On User Perception Of LLM-based Conversational Agents
AI总结：
本文通过混合方法被试内实验研究具身与非具身大语言模型对话智能体在协作任务中的用户感知，发现具身反而降低能力评价并增强谄媚感知，挑战了具身必然提升可信度的传统观点。
摘要：
对话智能体（CAs）中的具身是指这些智能体的物理或视觉表征，它会显著影响用户的感知与交互。已有研究很少考察在非层级协作任务中，具身对使用现代大语言模型（LLMs）的对话智能体的感知所产生的影响，而随着更强大的模型广泛可供一般使用，非层级协作任务正成为对话智能体的常见应用场景。为弥合这一研究空白，我们开展了一项混合方法、被试内设计的研究，探讨用户在协作任务中如何感知具身与非具身形式的基于大语言模型的对话智能体。结果表明，在量化评价上，非具身智能体在能力维度获得的评分显著优于具身智能体；在定性反馈中，许多参与者认为具身对话智能体比非具身对话智能体更倾向于谄媚逢迎。基于先前关于用户对大语言模型谄媚行为和拟人化特征感知的研究，我们提出理论解释：具身通常对对话智能体可信度感知产生的积极影响，在存在谄媚行为时可能转变为负面影响。这一现象的意义在于，与直觉和现有文献相反，如果对话智能体存在谄媚倾向，具身并不是提升其感知可信度的直接途径。
英文摘要：
Embodiment in conversational agents (CAs) refers to the physical or visual representation of these agents, which can significantly influence user perception and interaction. Limited work has been done examining the effect of embodiment on the perception of CAs utilizing modern large language models (LLMs) in non-hierarchical cooperative tasks, a common use case of CAs as more powerful models become widely available for general use. To bridge this research gap, we conducted a mixed-methods within-subjects study on how users perceive LLM-based CAs in cooperative tasks when embodied and non-embodied. The results show that the non-embodied agent received significantly better quantitative appraisals for competence than the embodied agent, and in qualitative feedback, many participants believed that the embodied CA was more sycophantic than the non-embodied CA. Building on prior work on users' perceptions of LLM sycophancy and anthropomorphic features, we theorize that the typically-positive impact of embodiment on perception of CA credibility can become detrimental in the presence of sycophancy. The implication of such a phenomenon is that, contrary to intuition and existing literature, embodiment is not a straightforward way to improve a CA's perceived credibility if there exists a tendency to sycophancy.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
