GazeNoter：通过注视选择 LLM 建议以匹配用户意图的协同驾驶 AR 笔记系统
GazeNoter: Co-Piloted AR Note-Taking via Gaze Selection of LLM Suggestions to Match Users' Intentions
AI总结：
本文提出 AI 协同驾驶的 AR 系统 GazeNoter，通过 AR 头显利用注视选择并调整 LLM 生成的建议以匹配用户意图，实现实时笔记，并在静态和移动场景下验证了其可用性。
摘要：
在演讲和讨论期间，记笔记至关重要，不仅用于后续的总结和整理，还用于问答环节中实时的提问和观点提醒，或在讨论中及时做出贡献。在智能手机上手动打字记笔记可能会分散注意力并增加用户的认知负荷。虽然大型语言模型（LLMs）被用于自动生成摘要和重点，但在没有用户输入或交互的情况下，人工智能（AI）生成的内容可能无法匹配用户的意图。因此，我们提出了一种 AI 协同驾驶的增强现实（AR）系统 GazeNoter，允许用户在 AR 头戴设备上通过注视快速选择多样化的 LLM 生成的建议，以进行实时记笔记。GazeNoter 利用 AR 头戴设备作为媒介，让用户快速调整 LLM 输出以匹配其意图，形成了一个用户在环的 AI 系统，适用于上下文内和上下文外的笔记。我们进行了两项用户研究，分别验证了 GazeNoter 在静态坐姿条件下参加演讲，以及在移动行走条件下进行行走会议和讨论时的可用性。
英文摘要：
Note-taking is critical during speeches and discussions, serving not only for later summarization and organization but also for real-time question and opinion reminding in question-and-answer sessions or timely contributions in discussions. Manually typing on smartphones for note-taking could be distracting and increase cognitive load for users. While large language models (LLMs) are used to automatically generate summaries and highlights, the content generated by artificial intelligence (AI) may not match users' intentions without user input or interaction. Therefore, we propose an AI-copiloted augmented reality (AR) system, GazeNoter, to allow users to swiftly select diverse LLM-generated suggestions via gaze on an AR headset for real-time note-taking. GazeNoter leverages an AR headset as a medium for users to swiftly adjust the LLM output to match their intentions, forming a user-in-the-loop AI system for both within-context and beyond-context notes. We conducted two user studies to verify the usability of GazeNoter in attending speeches in a static sitting condition and walking meetings and discussions in a mobile walking condition, respectively.
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。
