Ever-Evolving Memory by Blending and Refining the Past
This work addresses the issue of poor conversation quality in chatbots for users by improving long-term memory, though it appears incremental as it builds on existing memory approaches.
The authors tackled the problem of large language models lacking long-term memory in chatbots, which leads to missed user information and redundant questions, by proposing CREEM, a memory system that blends past memories and refines outdated information, resulting in enhanced memory and response qualities in multi-session dialogues.
For a human-like chatbot, constructing a long-term memory is crucial. However, current large language models often lack this capability, leading to instances of missing important user information or redundantly asking for the same information, thereby diminishing conversation quality. To effectively construct memory, it is crucial to seamlessly connect past and present information, while also possessing the ability to forget obstructive information. To address these challenges, we propose CREEM, a novel memory system for long-term conversation. Improving upon existing approaches that construct memory based solely on current sessions, CREEM blends past memories during memory formation. Additionally, we introduce a refining process to handle redundant or outdated information. Unlike traditional paradigms, we view responding and memory construction as inseparable tasks. The blending process, which creates new memories, also serves as a reasoning step for response generation by informing the connection between past and present. Through evaluation, we demonstrate that CREEM enhances both memory and response qualities in multi-session personalized dialogues.