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Creators/Authors contains: "Xia, Eric"

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  1. ; (, NAACL, aclanthology.org)
    A two-part affine approximation has been found to be a good approximation for trans- former computations over certain subject- object relations. Adapting the Bigger Analogy Test Set, we show that the linear transforma- tion W s, where s is a middle layer representa- tion of a subject token and W is derived from model derivatives, is also able to accurately re- produce 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. 
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    Free, publicly-accessible full text available March 1, 2026