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arXiv · 2405.16504

Explaining Modern Gated-Linear RNNs via a Unified Implicit Attention Formulation

Itamar Zimerman·Ameen Ali·Lior Wolf
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Abstract

Recent advances in efficient sequence modeling have led to attention-free layers, such as Mamba, RWKV, and various gated RNNs, all featuring sub-quadratic complexity in sequence length and excellent scaling properties, enabling the construction of a new type of foundation models. In this paper, we present a unified view of these models, formulating such layers as implicit causal self-attention layers. The formulation includes most of their sub-components and is not limited to a specific part of the architecture. The framework compares the underlying mechanisms on similar grounds for different layers and provides a direct means for applying explainability methods. Our experiments show that our attention matrices and attribution method outperform an alternative and a more limited formulation that was recently proposed for Mamba. For the other architectures for which our method is the first to provide such a view, our method is effective and competitive in the relevant metrics compared to the results obtained by state-of-the-art Transformer explainability methods. Our code is publicly available.

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Itamar Zimerman, Ameen Ali, Lior Wolf. 2024-05-26. Explaining Modern Gated-Linear RNNs via a Unified Implicit Attention Formulation. https://arxiv.org/abs/2405.16504

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