[2024 Best AI Paper] MemLong: Memory-Augmented Retrieval for Long Text Modeling

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This video was created using https://paperspeech.com. If you’d like to create explainer videos for your own papers, please visit the website! Title: MemLong: Memory-Augmented Retrieval for Long Text Modeling Authors: Weijie Liu, Zecheng Tang, Juntao Li, Kehai Chen, Min Zhang Abstract: Recent advancements in Large Language Models (LLMs) have yielded remarkable success across diverse fields. However, handling long contexts remains a significant challenge for LLMs due to the quadratic time and space complexity of attention mechanisms and the growing memory consumption of the key-value cache during generation. This work introduces MemLong: Memory-Augmented Retrieval for Long Text Generation, a method designed to enhance the capabilities of long-context language modeling by utilizing an external retriever for historical information retrieval. MemLong combines a non-differentiable ``ret-mem'' module with a partially trainable decoder-only language model and introduces a fine-grained, cont