Revolutionary advancements in Large Language Models have drastically reshaped
our interactions with artificial intelligence systems. Despite this, a notable
hindrance remains-the deficiency of a long-term memory mechanism within these
models. This shortfall becomes increasingly evident in situations demanding
sustained interaction, such as personal companion systems and psychological
counseling. Therefore, we propose MemoryBank, a novel memory mechanism tailored
for LLMs. MemoryBank enables the models to summon relevant memories,
continually evolve through continuous memory updates, comprehend, and adapt to
a user personality by synthesizing information from past interactions. To mimic
anthropomorphic behaviors and selectively preserve memory, MemoryBank
incorporates a memory updating mechanism, inspired by the Ebbinghaus Forgetting
Curve theory, which permits the AI to forget and reinforce memory based on time
elapsed and the relative significance of the memory, thereby offering a
human-like memory mechanism. MemoryBank is versatile in accommodating both
closed-source models like ChatGPT and open-source models like ChatGLM. We
exemplify application of MemoryBank through the creation of an LLM-based
chatbot named SiliconFriend in a long-term AI Companion scenario. Further tuned
with psychological dialogs, SiliconFriend displays heightened empathy in its
interactions. Experiment involves both qualitative analysis with real-world
user dialogs and quantitative analysis with simulated dialogs. In the latter,
ChatGPT acts as users with diverse characteristics and generates long-term
dialog contexts covering a wide array of topics. The results of our analysis
reveal that SiliconFriend, equipped with MemoryBank, exhibits a strong
capability for long-term companionship as it can provide emphatic response,
recall relevant memories and understand user personality.

提出了 MemoryBank，这是一种采用 Ebbinghaus 遗忘曲线中的记忆更新机制的新型记忆机制，旨在解决长期交互中 LLM 的记忆缺陷问题，并通过与心理对话调整提高其同理心。通过实验研究，证明了 MemoryBank 能够成功提高聊天机器人在长期 AI Companion 中的表现能力。