The Hidden Attention of Mamba Models

• URL: http://arxiv.org/abs/2403.01590v2
• Date: Sun, 31 Mar 2024 14:31:14 GMT
• Title: The Hidden Attention of Mamba Models
• Authors: Ameen Ali, Itamar Zimerman, Lior Wolf,
• Abstract summary: The Mamba layer offers an efficient selective state space model (SSM) that is highly effective in modeling multiple domains. We show that such models can be viewed as attention-driven models. This new perspective enables us to empirically and theoretically compare the underlying mechanisms to that of the self-attention layers in transformers.
• Score: 54.50526986788175
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The Mamba layer offers an efficient selective state space model (SSM) that is highly effective in modeling multiple domains, including NLP, long-range sequence processing, and computer vision. Selective SSMs are viewed as dual models, in which one trains in parallel on the entire sequence via an IO-aware parallel scan, and deploys in an autoregressive manner. We add a third view and show that such models can be viewed as attention-driven models. This new perspective enables us to empirically and theoretically compare the underlying mechanisms to that of the self-attention layers in transformers and allows us to peer inside the inner workings of the Mamba model with explainability methods. Our code is publicly available.

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