Abstract: Unsupervised domain adaptation (UDA) involves learning class semantics from labeled data within a source domain that generalize to an unseen target domain. UDA methods are particularly impactful for semantic segmentation, where annotations are more difficult to collect than in image classification. Despite recent advances in large-scale vision-language representation learning, UDA methods for segmentation have not taken advantage of the domain-agnostic properties of text. To address this, we present a novel covariance-based pixel-text loss, CoPT, that uses domain-agnostic text embeddings to learn domain-invariant features in an image segmentation encoder. The text embeddings are generated through our LLM Domain Template process, where an LLM is used to generate source and target domain descriptions that are combined and fed to a frozen CLIP model. In experiments on GTA$\rightarrow$Cityscapes and Synthia$\rightarrow$Cityscapes, we show that a model trained using CoPT achieves the new state of the art performance on UDA for segmentation.