Transformers have had a profound impact on the world of language processing and computer vision.
As efforts to answer the million-dollar question of ``How does a Transformer learn?" have been increasing, existing interpretability studies primarily analyze representations at isolated layers or the network as a whole, while the developmental evolution of individual representations and its manifolds across transformer layers remains underexplored.
With this work, we aim at providing a comprehensive analysis of the evolution of representations as the representation point cloud transforms across the layers;
thereby attempting to isolate layers or establish a trend which comes closer to justifying how and when raw input representations evolve into task-relevant feature representations.
Thus, Transformer Geometry Observatory-TGO-IV introduces a topological framework for analysing the evolution of Transformer representations through the lens of Persistent Homology.
Rather than studying local geometric properties alone, TGO-IV constructs Vietoris--Rips simplicial complexes from token-level representation point clouds and investigates the evolution of their persistent topological signatures across Transformer layers.
The proposed framework comprises complementary topological observatories including
- Persistence Diagrams
- Barcode Diagrams
- Betti Curves
- Persistence Landscapes
- Bottleneck Distance
- Wasserstein Distance
enabling a comprehensive analysis of how the global topology of representation point clouds develops throughout the forward pass.