MemoryBank：通过长期记忆增强大型语言模型
MemoryBank: Enhancing Large Language Models with Long-Term Memory
AI总结：
针对大型语言模型缺乏长期记忆机制的问题，提出MemoryBank，通过基于Ebbinghaus遗忘曲线的记忆更新机制实现记忆的调用、遗忘与强化，并兼容多种模型，在长期AI伴侣场景中展现出优异的共情与陪伴能力。
摘要：
大型语言模型的革命性进步极大地重塑了我们与人工智能系统的交互方式。尽管如此，一个显著的障碍依然存在——这些模型缺乏长期记忆机制。在需要持续互动的场景中，例如个人伴侣系统和心理咨询，这一缺陷变得日益明显。因此，我们提出了MemoryBank，一种专为LLM量身定制的新型记忆机制。MemoryBank使模型能够调用相关记忆，通过持续的记忆更新不断进化，并通过综合过去交互的信息来理解和适应用户个性。为了模仿拟人化行为并选择性地保留记忆，MemoryBank结合了受Ebbinghaus遗忘曲线理论启发的记忆更新机制，允许AI根据时间流逝和记忆的相对重要性来遗忘和强化记忆，从而提供一种类人的记忆机制。MemoryBank能够灵活适应ChatGPT等闭源模型和ChatGLM等开源模型。我们通过在长期AI伴侣场景中创建一个名为SiliconFriend的基于LLM的聊天机器人来展示MemoryBank的应用。经过心理对话的进一步微调，SiliconFriend在交互中表现出更强的同理心。实验包括对真实用户对话的定性分析和对模拟对话的定量分析。在后者中，ChatGPT扮演具有不同特征的用户，并生成涵盖广泛主题的长期对话上下文。我们的分析结果表明，配备了MemoryBank的SiliconFriend表现出强大的长期陪伴能力，因为它能够提供共情回应、回忆相关记忆并理解用户个性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
