Memoro: Using Large Language Models to Realize a Concise Interface for Real-Time Memory Augmentation
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3613904.3642450.pdf
Size
1006.34 KB
Format
Adobe PDF
Checksum (MD5)
289f8402e2d1a50b464793ca447e9db3
Author(s)
Zulfikar, Wazeer Deen
Chan, Samantha
Maes, Pattie
Date Issued
May 11, 2024
Publisher
ACM
Citation
Zulfikar, Wazeer Deen, Chan, Samantha and Maes, Pattie. 2024. "Memoro: Using Large Language Models to Realize a Concise Interface for Real-Time Memory Augmentation."
Version
Final published version
Abstract
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.
Description
CHI '24: Proceedings of the CHI Conference on Human Factors in Computing Systems May 11–16, 2024, Honolulu, HI, USA
MIT Department
Massachusetts Institute of Technology. Media Laboratory
Terms of Use
Creative Commons Attribution-Noncommercial-ShareAlike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1145/3613904.3642450
