Do all deep learning architectures learn features hierarchically?

CNNs are a famous example of hierarchical processing. Lower-level features such as edges are detected in the earlier layers while higher-level features such as the presence of a face are detected deeper in the network. All deep learning architectures share a common thing (excerpt taken from the Deep Learning book ): The modern term “deep learning” goes beyond the neuroscientific perspective on the current breed of machine learning models. It appeals to a more general principle of learning multiple levels of composition , which can be applied in machine learning frameworks that are not necessarily neurally inspired. My question is the following: Is learning multiple levels of composition equivalent to hierarchical processing/hierarchical feature extraction? If yes, what kind of hierarchies are extracted in fields other than computer vision? For example, what kind of features are extracted hierarchically from a transformer-based architecture or a graph neural network?