Memolet: Reifying the Reuse of User-AI Conversational Memories

Conversational ChatbotsHuman-LLM CollaborationAI/ML Researchers & EngineersHCI Researchers

As users engage more frequently with AI conversational agents, conversations may exceed their memory capacity, leading to failures in correctly leveraging certain memories for tailored responses. However, in finding past memories that can be reused or referenced, users need to retrieve relevant information in various conversations and articulate to the AI their intention to reuse these memories. To support this process, we introduce Memolet, an interactive object that reifies memory reuse. Users can directly manipulate Memolet to specify which memories to reuse and how to use them. We developed a system demonstrating Memolet's interaction across various memory reuse stages, including memory extraction, organization, prompt articulation, and generation refinement. We examine the system's usefulness with an N=12 within-subject study and provide design implications for future systems that support user-AI conversational memory reusing.

Quick Actions

Share

Share this page

ios_share

https://hci.top/en/papers/uist/170751/2024

AdRecommended

Learn AI Coding at CodeNow

open_in_newOpen DOI Link
DOI: https://doi.org/10.1145/3654777.3676388
At a Glance

Paper Snapshot

fact_check
dataset
Source
UIST
calendar_month
Year
2024
emoji_events
Award
No award tagged
group
Authors
2 authors
sell
Subtopics
Conversational Chatbots, Human-LLM Collaboration
work
Professions
AI/ML Researchers & Engineers, HCI Researchers
article
Content Status
Abstract only
hub
Related Papers
10 related papers