  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handle redundant or outdated information.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.
