MemLong: Memory-Augmented Retrieval for Long Text Modeling

TL;DR

MemLong introduces a memory-augmented retrieval method that significantly improves long text modeling in language models, enabling context lengths up to 80k tokens while outperforming state-of-the-art models.

Contribution

The paper presents a novel retrieval-augmented approach combining a non-differentiable memory module with a trainable decoder to handle extremely long contexts efficiently.

Findings

Outperforms existing state-of-the-art long-context models

Extends context length from 4k to 80k tokens on a single GPU

Demonstrates superior performance on multiple benchmarks

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, controllable retrieval attention mechanism that leverages semantic-level relevant chunks. Comprehensive evaluations on multiple long-context language modeling benchmarks demonstrate that MemLong consistently outperforms other state-of-the-art LLMs. More importantly, MemLong can extend the context length on a single 3090 GPU from 4k up to 80k. Our code is available at https://github.com/Bui1dMySea/MemLong