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

Itamar Zimerman, Ameen Ali Ali, Lior Wolf

Keywords: Explainability, Interpretability, Gated-Linear RNNs, Attention-free, Mamba

TL;DR: Explaining Modern Gated-Linear RNNs via a Unified Implicit Attention Formulation

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 attached as a supplement.

Primary Area: interpretability and explainable AI

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Submission Number: 4752