The ideal long-term memory mechanism for Large Language Model (LLM) based chatbots, would lay the foundation for continual learning, complex reasoning and allow sequential and temporal dependencies to be learnt. Creating this type of memory mechanism is an extremely challenging problem. In this paper we explore different methods of achieving the effect of long-term memory. We propose a new architecture focused on creating adaptable and updatable long-term memory for AGI systems. We demonstrate through various experiments the benefits of the RecallM architecture, particularly the improved temporal understanding of knowledge it provides.

翻译：理想的大型语言模型聊天机器人长期记忆机制，将为持续学习、复杂推理以及序列与时序依赖关系的习得奠定基础。构建此类记忆机制是一个极具挑战性的问题。本文探索了实现长期记忆效果的不同方法，并提出了一种专注于为通用人工智能系统创建可自适应、可更新的长期记忆的新架构。通过多项实验，我们展示了RecallM架构的优势，尤其是其在提升知识时序理解能力方面的显著效果。