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

Artificial Intelligence and Digital Twins play an integral role in driving
innovation in the domain of intelligent driving. Long short-term memory (LSTM)
is a leading driver in the field of lane change prediction for manoeuvre
anticipation. However, the decision-making process of such models is complex
and non-transparent, hence reducing the trustworthiness of the smart solution.
This work presents an innovative approach and a technical implementation for
explaining lane change predictions of layer normalized LSTMs using Layer-wise
Relevance Propagation (LRP). The core implementation includes consuming live
data from a digital twin on a German highway, live predictions and explanations
of lane changes by extending LRP to layer normalized LSTMs, and an interface
for communicating and explaining the predictions to a human user. We aim to
demonstrate faithful, understandable, and adaptable explanations of lane change
prediction to increase the adoption and trustworthiness of AI systems that
involve humans. Our research also emphases that explainability and
state-of-the-art performance of ML models for manoeuvre anticipation go hand in
hand without negatively affecting predictive effectiveness.

该研究提出了一种创新的方法和技术实现，使用层次规范化的 LSTM 和基于 LRP 的方法来解释车道变更预测。其目的是为了增加人类信赖并减少人工智能系统的错误率。

基于标准化 LSTM 和层级相关传播的数字孪生道路可解释的在线变道预测

Explainable Online Lane Change Predictions on a Digital Twin with a Layer Normalized LSTM and Layer-wise Relevance Propagation

While neural networks have acted as a strong unifying force in the design of
modern AI systems, the neural network architectures themselves remain highly
heterogeneous due to the variety of tasks to be solved. In this chapter, we
explore how to adapt the Layer-wise Relevance Propagation (LRP) technique used
for explaining the predictions of feed-forward networks to the LSTM
architecture used for sequential data modeling and forecasting. The special
accumulators and gated interactions present in the LSTM require both a new
propagation scheme and an extension of the underlying theoretical framework to
deliver faithful explanations.

本章节探究如何使 Layer-wise Relevance Propagation (LRP) 技术适用于序列数据建模和预测所使用的 LSTM 架构，需要更新传播方案并扩展基础理论框架以提供准确的解释。