  High-dimensional token embeddings underpin Large Language Models (LLMs), as
they can capture subtle semantic information and significantly enhance the
modelling of complex language patterns. However, the associated high
dimensionality also introduces considerable model parameters, and a
prohibitively high model storage. To address this issue, this work proposes an
approach based on the Tensor-Train Decomposition (TTD), where each token
embedding is treated as a Matrix Product State (MPS) that can be efficiently
computed in a distributed manner. The experimental results on GPT-2 demonstrate
that, through our approach, the embedding layer can be compressed by a factor
of up to 38.40 times, and when the compression factor is 3.31 times, even
produced a better performance than the original GPT-2 model.
