arxiv Preprint – Walking Down the Memory Maze: Beyond Context Limit through Interactive Reading

In this episode we discuss Walking Down the Memory Maze: Beyond Context Limit through Interactive Reading
by Howard Chen, Ramakanth Pasunuru, Jason Weston, Asli Celikyilmaz. The paper introduces MEMWALKER, an approach to address the limitations of the self-attention mechanism in large language models (LLMs) when processing long sequences. MEMWALKER treats the LLM as an interactive agent that iteratively reads the text, processing the long context into a tree of summary nodes. The model is then able to navigate this tree to gather relevant information and respond to queries. The paper demonstrates that MEMWALKER outperforms existing methods for long-text question answering tasks and enhances explainability by highlighting reasoning steps and relevant text segments.