  Recent sequence modeling approaches using Selective State Space Sequence
Models, referred to as Mamba models, have seen a surge of interest. These
models allow efficient processing of long sequences in linear time and are
rapidly being adopted in a wide range of applications such as language
modeling, demonstrating promising performance. To foster their reliable use in
real-world scenarios, it is crucial to augment their transparency. Our work
bridges this critical gap by bringing explainability, particularly Layer-wise
Relevance Propagation (LRP), to the Mamba architecture. Guided by the axiom of
relevance conservation, we identify specific components in the Mamba
architecture, which cause unfaithful explanations. To remedy this issue, we
propose MambaLRP, a novel algorithm within the LRP framework, which ensures a
more stable and reliable relevance propagation through these components. Our
proposed method is theoretically sound and excels in achieving state-of-the-art
explanation performance across a diverse range of models and datasets.
Moreover, MambaLRP facilitates a deeper inspection of Mamba architectures,
uncovering various biases and evaluating their significance. It also enables
the analysis of previous speculations regarding the long-range capabilities of
Mamba models.
