Enhanced Structured State Space Models via Grouped FIR Filtering and Attention Sink Mechanisms

• URL: http://arxiv.org/abs/2408.00244v1
• Date: Thu, 1 Aug 2024 02:49:58 GMT
• Title: Enhanced Structured State Space Models via Grouped FIR Filtering and Attention Sink Mechanisms
• Authors: Tian Meng, Yang Tao, Wuliang Yin,
• Abstract summary: We propose an advanced architecture that mitigates challenges by decomposing A-multiplications into multiple groups. Inspired by the "attention sink" phenomenon identified in streaming language models, we incorporate a similar mechanism to enhance the stability and performance of our model.
• Score: 0.6718184400443239
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Structured State Space Models (SSMs) have emerged as compelling alternatives to Transformer architectures, offering linear-time complexity and superior performance in various sequence modeling tasks. Despite their advantages, SSMs like the original Mamba-2 face training difficulties due to the sensitivities introduced by the extended series of recurrent matrix multiplications. In this paper, we propose an advanced architecture that mitigates these challenges by decomposing A-multiplications into multiple groups and optimizing positional encoding through Grouped Finite Impulse Response (FIR) filtering. This new structure, denoted as Grouped FIR-enhanced SSM (GFSSM), employs semiseparable matrices for efficient computation. Furthermore, inspired by the "attention sink" phenomenon identified in streaming language models, we incorporate a similar mechanism to enhance the stability and performance of our model over extended sequences. Our approach further bridges the gap between SSMs and Transformer architectures, offering a viable path forward for scalable and high-performing sequence modeling.

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