  Large language models (LLMs) have shown remarkable capabilities, but still
struggle with processing extensive contexts, limiting their ability to maintain
coherence and accuracy over long sequences. In contrast, the human brain excels
at organising and retrieving episodic experiences across vast temporal scales,
spanning a lifetime. In this work, we introduce EM-LLM, a novel approach that
integrates key aspects of human episodic memory and event cognition into LLMs,
enabling them to effectively handle practically infinite context lengths while
maintaining computational efficiency. EM-LLM organises sequences of tokens into
coherent episodic events using a combination of Bayesian surprise and
graph-theoretic boundary refinement in an on-line fashion. When needed, these
events are retrieved through a two-stage memory process, combining
similarity-based and temporally contiguous retrieval for efficient and
human-like access to relevant information. Experiments on the LongBench dataset
demonstrate EM-LLM's superior performance, outperforming the state-of-the-art
InfLLM model with an overall relative improvement of 4.3% across various tasks,
including a 33% improvement on the PassageRetrieval task. Furthermore, our
analysis reveals strong correlations between EM-LLM's event segmentation and
human-perceived events, suggesting a bridge between this artificial system and
its biological counterpart. This work not only advances LLM capabilities in
processing extended contexts but also provides a computational framework for
exploring human memory mechanisms, opening new avenues for interdisciplinary
research in AI and cognitive science.
