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Efficient Attention Mechanisms for Long-Context Language Models

Abstract

We present a novel attention mechanism that achieves state-of-the-art performance on long-context tasks while reducing computational complexity from O(n²) to O(n log n). Our approach, called LinearFlash, combines linear attention with flash attention techniques to enable efficient processing of sequences up to 128K tokens. Experiments on several benchmarks demonstrate significant improvements in both speed and quality.

1. Introduction

The transformer architecture has revolutionized natural language processing, but its quadratic attention complexity limits applicability to long sequences. Recent work has explored various approaches to address this limitation, including sparse attention patterns, linear attention variants, and memory-efficient implementations.