A two-part affine approximation has been found to be a good approximation for transformer computations over certain subject object relations. Adapting the Bigger Analogy Test Set, we show that the linear transformation Ws, where s is a middle layer representation of a subject token and W is derived from model derivatives, is also able to accurately reproduce final object states for many relations. This linear technique is able to achieve 90% faithfulness on morphological relations, and we show similar findings multi-lingually and across models. Our findings indicate that some conceptual relationships in language models, such as morphology, are readily interpretable from latent space, and are sparsely encoded by cross-layer linear transformations.
@article{arxiv.2507.14640,
title = {Linear Relational Decoding of Morphology in Language Models},
author = {Eric Xia and Jugal Kalita},
journal= {arXiv preprint arXiv:2507.14640},
year = {2025}
}