People have to remember an ever-expanding volume of information. Wearables that use information capture and retrieval for memory augmentation can help but can be disruptive and cumbersome in real-world tasks, such as in social settings. To address this, we developed Memoro, a wearable audio-based memory assistant with a concise user interface. Memoro uses a large language model (LLM) to infer the user's memory needs in a conversational context, semantically search memories, and present minimal suggestions. The assistant has two interaction modes: Query Mode for voicing queries and Queryless Mode for on-demand predictive assistance, without explicit query. Our study of (N=20) participants engaged in a real-time conversation demonstrated that using Memoro reduced device interaction time and increased recall confidence while preserving conversational quality. We report quantitative results and discuss the preferences and experiences of users. This work contributes towards utilizing LLMs to design wearable memory augmentation systems that are minimally disruptive.

翻译：摘要：随着信息量的持续膨胀，人们需要记住日益增多的内容。用于记忆增强的可穿戴信息捕捉与检索设备虽能提供帮助，但在社交场景等实际任务中往往存在干扰性强、操作繁琐的问题。为此，我们开发了Memoro——一款基于可穿戴音频的简洁交互式记忆辅助系统。该系统通过大型语言模型推断用户在对话场景中的记忆需求，实现记忆语义检索并呈现精简建议。系统提供两种交互模式：显式查询模式允许用户语音提问，而隐式查询模式则无需明确指令即可按需提供预测性辅助。在包含20名参与者的实时对话实验中，使用Memoro有效缩短了设备交互时间，提升了回忆准确率，同时保持了对话质量。我们报告了量化实验结果，并探讨了用户偏好与使用体验。本研究表明大型语言模型在构建低干扰性可穿戴记忆增强系统中具有重要应用价值。