Unlocking the Secrets of Linear Complexity Sequence Model from A Unified Perspective

• URL: http://arxiv.org/abs/2405.17383v1
• Date: Mon, 27 May 2024 17:38:55 GMT
• Title: Unlocking the Secrets of Linear Complexity Sequence Model from A Unified Perspective
• Authors: Zhen Qin, Xuyang Shen, Weigao Sun, Dong Li, Stan Birchfield, Richard Hartley, Yiran Zhong,
• Abstract summary: The Linear Complexity Sequence Model (LCSM) unites various sequence modeling techniques with linear complexity. We segment the modeling processes of these models into three distinct stages: Expand, Oscillation, and Shrink. We perform experiments to analyze the impact of different stage settings on language modeling and retrieval tasks.
• Score: 26.479602180023125
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present the Linear Complexity Sequence Model (LCSM), a comprehensive solution that unites various sequence modeling techniques with linear complexity, including linear attention, state space model, long convolution, and linear RNN, within a single framework. The goal is to enhance comprehension of these models by analyzing the impact of each component from a cohesive and streamlined viewpoint. Specifically, we segment the modeling processes of these models into three distinct stages: Expand, Oscillation, and Shrink (EOS), with each model having its own specific settings. The Expand stage involves projecting the input signal onto a high-dimensional memory state. This is followed by recursive operations performed on the memory state in the Oscillation stage. Finally, the memory state is projected back to a low-dimensional space in the Shrink stage. We perform comprehensive experiments to analyze the impact of different stage settings on language modeling and retrieval tasks. Our results show that data-driven methods are crucial for the effectiveness of the three stages in language modeling, whereas hand-crafted methods yield better performance in retrieval tasks.

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