Interpretability has emerged as a new buzzword and research focus in AI system development and deployment. Although deep learning models have achieved remarkable success in recent years, the question of how these models work remains underexplored and poorly understood — mainly due to a lack of ground truth information regarding the models’ complex internal mechanisms. In the new paper Tracr: Compiled Transformers as a Laboratory for Interpretability, a res