Memoro:使用大型语言模型实现实时内存增强的简洁界面

ArXiv Pub Date : 2024-03-04 DOI:10.1145/3613904.3642450
Wazeer Zulfikar, Samantha Chan, Pattie Maes
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引用次数: 0

摘要

人们需要记住越来越多的信息。利用信息捕捉和检索来增强记忆的可穿戴设备可以起到帮助作用,但在实际任务中(如社交场合)可能会造成干扰和麻烦。为了解决这个问题,我们开发了一款基于音频的可穿戴记忆助手 Memoro,它拥有简洁的用户界面。Memoro 使用大语言模型(LLM)来推断用户在对话语境中的记忆需求,对记忆进行语义搜索,并提出最简单的建议。该助手有两种交互模式:查询模式用于语音查询,无查询模式用于按需提供预测性帮助,无需明确查询。我们对参与实时对话的参与者(20 人)进行的研究表明,使用 Memoro 可以减少设备交互时间,提高回忆信心,同时保持对话质量。我们报告了定量结果,并讨论了用户的偏好和体验。这项工作有助于利用 LLM 设计干扰最小的可穿戴式记忆增强系统。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Memoro: Using Large Language Models to Realize a Concise Interface for Real-Time Memory Augmentation
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.
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