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.

借助相关性传播的层次和 Mamba 架构的分析，我们提出了 MambaLRP 算法，以增强深度学习模型的可解释性，并取得了多个模型和数据集上的最先进解释性能。

MambaLRP: 解释选择性状态空间序列模型

MambaLRP: Explaining Selective State Space Sequence Models

Transformer-based models excel in various natural language processing (NLP)
tasks, attracting countless efforts to explain their inner workings. Prior
methods explain Transformers by focusing on the raw gradient and attention as
token attribution scores, where non-relevant information is often considered
during explanation computation, resulting in confusing results. In this work,
we propose highlighting the important information and eliminating irrelevant
information by a refined information flow on top of the layer-wise relevance
propagation (LRP) method. Specifically, we consider identifying syntactic and
positional heads as important attention heads and focus on the relevance
obtained from these important heads. Experimental results demonstrate that
irrelevant information does distort output attribution scores and then should
be masked during explanation computation. Compared to eight baselines on both
classification and question-answering datasets, our method consistently
outperforms with over 3\% to 33\% improvement on explanation metrics, providing
superior explanation performance. Our anonymous code repository is available
at: this https URL

提出了一种在层间相关传播 (LRP) 方法的基础上通过细化信息流来突出重要信息并消除无关信息，实验结果表明，与八个基准方法相比，在分类和问答数据集上我们的方法始终表现出超过 3% 到 33% 的解释指标的提升，提供了更好的解释性能。

通过点亮重要信息更好地解释 Transformers

Better Explain Transformers by Illuminating Important Information

Graph Neural Networks (GNNs) achieve state-of-the-art performance in various
graph-related tasks. However, the black-box nature often limits their
interpretability and trustworthiness. Numerous explainability methods have been
proposed to uncover the decision-making logic of GNNs, by generating underlying
explanatory substructures. In this paper, we conduct a comprehensive review of
the existing explanation methods for GNNs from the perspective of graph
generation. Specifically, we propose a unified optimization objective for
generative explanation methods, comprising two sub-objectives: Attribution and
Information constraints. We further demonstrate their specific manifestations
in various generative model architectures and different explanation scenarios.
With the unified objective of the explanation problem, we reveal the shared
characteristics and distinctions among current methods, laying the foundation
for future methodological advancements. Empirical results demonstrate the
advantages and limitations of different explainability approaches in terms of
explanation performance, efficiency, and generalizability.

结合图生成的角度，本文综述了用于图神经网络的解释方法，并就生成解释方法提出了统一的优化目标，包括归因和信息约束两个子目标。通过揭示现有方法的共享特性和差异，为未来的方法改进奠定了基础。实证结果对不同的解释方法在解释性能、效率和泛化能力方面的优势和局限性进行了验证。