Ever-Evolving Memory by Blending and Refining the Past
arxiv(2024)
摘要
For a human-like chatbot, constructing a long-term memory is crucial. A naive
approach for making a memory could be simply listing the summarized dialogue.
However, this can lead to problems when the speaker's status change over time
and contradictory information gets accumulated. It is important that the memory
stays organized to lower the confusion for the response generator. In this
paper, we propose a novel memory scheme for long-term conversation, CREEM.
Unlike existing approaches that construct memory based solely on current
sessions, our proposed model blending past memories during memory formation.
Additionally, we introduce refining process to handle redundant or outdated
information. This innovative approach seeks for overall improvement and
coherence of chatbot responses by ensuring a more informed and dynamically
evolving long-term memory.
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