The Double Helix inside the NLP Transformer
Jason H. J. Lu Qingzhen Guo
We introduce a framework for analyzing various types of information in an NLP
Transformer. In this approach, we distinguish four layers of information:
positional, syntactic, semantic, and contextual. We also argue that the common
practice of adding positional information to semantic embedding is sub-optimal
and propose instead a Linear-and-Add approach. Our analysis reveals an
autogenetic separation of positional information through the deep layers. We
show that the distilled positional components of the embedding vectors follow
the path of a helix, both on the encoder side and on the decoder side. We
additionally show that on the encoder side, the conceptual dimensions generate
Part-of-Speech (PoS) clusters. On the decoder side, we show that a di-gram
approach helps to reveal the PoS clusters of the next token. Our approach paves
a way to elucidate the processing of information through the deep layers of an
NLP Transformer.