[R] Traveling Words: A Geometric Interpretation of Transformers

Paper: [https://arxiv.org/abs/2309.07315](https://arxiv.org/abs/2309.07315)

Abstract:

>Transformers have significantly advanced the field of natural language processing, but comprehending their internal mechanisms remains a challenge. In this paper, we introduce a novel geometric perspective that elucidates the inner mechanisms of transformer operations. Our primary contribution is illustrating how layer normalization confines the latent features to a hyper-sphere, subsequently enabling attention to mold the semantic representation of words on this surface. This geometric viewpoint seamlessly connects established properties such as iterative refinement and contextual embeddings. …

abstract advanced attention challenge enabling features language language processing machinelearning natural natural language natural language processing normalization novel operations paper perspective processing representation semantic sphere transformer transformers