How can the interpretability of transformer models be improved?

Interpretability in transformer models can be improved through various techniques. One common approach is attention visualization, which allows researchers to see which parts of the input the model focuses on when making predictions. Tools like Attention Rollout aggregate attention scores to provide insights into how information flows through the layers. Layer-wise relevance propagation (LRP) can also be utilized to trace back decisions to input features. Additionally, researchers may employ probing tasks that examine the internal representations learned by different layers, allowing for a better understanding of what information is captured at each stage.