Researchers have recently suggested that models share common representations. In this work, we find that the token embeddings of language models exhibit common geometric structure. First, we find ``global'' similarities: token embeddings often share similar relative orientations. Next, we characterize local geometry in two ways: (1) by using Locally Linear Embeddings, and (2) by defining a simple measure for the intrinsic dimension of each token embedding. Our intrinsic dimension measure demonstrates that token embeddings lie on a lower dimensional manifold. We qualitatively show that tokens with lower intrinsic dimensions often have semantically coherent clusters, while those with higher intrinsic dimensions do not. Both characterizations allow us to find similarities in the local geometry of token embeddings. Perhaps most surprisingly, we find that alignment in token embeddings persists through the hidden states of language models, allowing us to develop an application for interpretability. Namely, we empirically demonstrate that steering vectors from one language model can be transferred to another, despite the two models having different dimensions.
View on arXiv@article{lee2025_2503.21073,
title={ Shared Global and Local Geometry of Language Model Embeddings },
author={ Andrew Lee and Melanie Weber and Fernanda Viégas and Martin Wattenberg },
journal={arXiv preprint arXiv:2503.21073},
year={ 2025 }
}