MemoryBank: Enhancing Large Language Models with Long-Term Memory
Wanjun Zhong1, Lianghong Guo1, Qiqi Gao2, He Ye3, Yanlin Wang1*
1 Sun Yat-Sen University 2 Harbin Institute of Technology
3 KTH Royal Institute of Technology {zhongwj25@mail2, wangylin36@mail, guolh8@mail2}.sysu.edu.com
Topics in Cognitive Science 00 (2023) 1–28
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This study proposes a new memory mechanism called MemoryBank that can be used to enhance the long-term memory capacity of LLMs.
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MemoryBank allows LLMs to store and retrieve past interactions, summarize events, and understand user personalities. It also includes a memory updating mechanism based on the Ebbinghaus Forgetting Curve, which is a psychological principle that describes how memory decays over time.
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The study shows that MemoryBank can be used to improve the performance of LLMs in tasks that require long-term interaction, such as AI companionship. [5-9]
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The authors of the study developed an AI chatbot called SiliconFriend that uses MemoryBank to provide more personalized and empathetic conversations. Their experiments show that SiliconFriend is able to recall past interactions, provide emotional support, and understand user personalities. [9-11]
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