探索沉浸式扩展现实中基于大语言模型的对话代理用户接受度与担忧
Exploring User Acceptance and Concerns toward LLM-powered Conversational Agents in Immersive Extended Reality
AI总结：
研究探讨了沉浸式扩展现实中基于大语言模型的对话代理的用户接受度与担忧，发现熟悉度和性别影响接受度，位置数据敏感性最高。
摘要：
生成式人工智能（AI）和大语言模型（LLMs）的快速发展，以及使它们可及的服务的出现，使大众开始将它们融入日常生活。扩展现实（XR）社区也试图将LLMs，特别是以对话代理的形式整合进来，以提升用户体验和任务效率。在与这些对话代理互动时，用户可能由于对话的自然流畅而轻易披露敏感信息，将此类对话数据与细粒度传感器数据结合可能引发新的隐私问题。为了解决这些问题，对技术接受和担忧的用户中心理解至关重要。因此，我们进行了一个大规模的众包研究，涉及1036名参与者，考察用户在XR环境中对LLM驱动的对话代理的决策过程，涉及XR设置类型、语音交互类型和数据处理位置等因素。我们发现，尽管用户普遍接受这些技术，但他们在安全、隐私、社会影响和信任方面表达了担忧。我们的结果表明，熟悉度起着关键作用，日常生成式AI使用与更高的接受度相关。相比之下，先前拥有XR设备的用户接受度较低，可能由于对设置的现有熟悉度。我们还发现，男性报告的接受度比女性更高，担忧较少。关于数据类型敏感性，位置数据引发了最大的担忧，而体温和虚拟物体状态则被认为是最不敏感的。总体而言，我们的研究强调了从业者必须有效地向用户传达其措施的重要性，因为用户可能会保持不信任。我们最后得出关于LLM驱动的XR的启示和建议。
英文摘要：
The rapid development of generative artificial intelligence (AI) and large language models (LLMs), and the availability of services that make them accessible, have led the general public to begin incorporating them into everyday life. The extended reality (XR) community has also sought to integrate LLMs, particularly in the form of conversational agents, to enhance user experience and task efficiency. When interacting with such conversational agents, users may easily disclose sensitive information due to the naturalistic flow of the conversations, and combining such conversational data with fine-grained sensor data may lead to novel privacy issues. To address these issues, a user-centric understanding of technology acceptance and concerns is essential. Therefore, to this end, we conducted a large-scale crowdsourcing study with 1036 participants, examining user decision-making processes regarding LLM-powered conversational agents in XR, across factors of XR setting type, speech interaction type, and data processing location. We found that while users generally accept these technologies, they express concerns related to security, privacy, social implications, and trust. Our results suggest that familiarity plays a crucial role, as daily generative AI use is associated with greater acceptance. In contrast, previous ownership of XR devices is linked to less acceptance, possibly due to existing familiarity with the settings. We also found that men report higher acceptance with fewer concerns than women. Regarding data type sensitivity, location data elicited the most significant concern, while body temperature and virtual object states were considered least sensitive. Overall, our study highlights the importance of practitioners effectively communicating their measures to users, who may remain distrustful. We conclude with implications and recommendations for LLM-powered XR.
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。
