Large Language Models need Episodic Memory

Overview & Synopsis

Large Language Models (LLMs) have made significant strides in natural language processing, yet they still struggle with maintaining consistent personas, tracking long-term dependencies, and adapting to new information during conversations. This paper argues that integrating episodic memory, a crucial component of human cognition, into LLMs is essential for addressing these limitations and advancing towards more human-like artificial intelligence. We first review the current progress and limitations of LLMs in memory retention and processing. Then, we explore recent insights from cognitive neuroscience regarding episodic memory, highlighting its role in human cognition. The paper demonstrates why LLMs need episodic memory, focusing on potential improvements in temporal cognition, autobiographical continuity, reality monitoring, and adaptive learning. We propose theoretical approaches for implementing episodic memory in LLMs, discussing challenges such as efficient storage and retrieval of episodic information, and suggest potential architectures inspired by the hippocampalcortical memory system. This research aims to bridge the gap between LLMs and human-like cognitive abilities, potentially revolutionizing both LLMs development and our understanding of human memory processes.