SOURCE-LINKED INTELLIGENCE
Analysing the Linearity of Linguistic Relations in Language Model Embedding Spaces
We propose a framework to analyse how strongly different linguistic relations are linearly encoded in language model embedding spaces. We formalise linear encoding via a constrained linear approximation over related and unrelated word pairs and apply this to an extended BATS dataset covering inflectional, derivational, lexicographic, and encyclopedic relations in GloVe, RoBERTa, and ModernBERT. Our experiments show near-perfect linear encodings for inflectional and derivational relations, but substantially higher errors for lexicographic and encyclopedic relations, especially for one-to-many a
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-18T11:53:43.000Z
First collected: 2026-09-23T13:51:27.104Z. This is not the publication date.