arxiv preprint - Human-like Episodic Memory for Infinite Context LLMs

In this episode, we discuss Human-like Episodic Memory for Infinite Context LLMs by Zafeirios Fountas, Martin A Benfeghoul, Adnan Oomerjee, Fenia Christopoulou, Gerasimos Lampouras, Haitham Bou-Ammar, Jun Wang. The paper introduces EM-LLM, an approach that enhances large language models (LLMs) by incorporating principles of human episodic memory and event cognition, enabling them to manage extensive contexts efficiently. EM-LLM uses Bayesian surprise and graph-theoretic boundary refinement to organize token sequences into episodic events and employs a two-stage memory process for effective retrieval. Experiments demonstrate that EM-LLM outperforms existing models on various tasks, showing significant improvement, and aligning well with human event perception, suggesting potential for interdisciplinary AI and cognitive science research.