2026
Steering Embedding Models with Geometric Rotation: Mapping Semantic Relationships Across Languages and Models
ICLR 2026poster
Understanding how language and embedding models encode semantic relationships is fundamental to model interpretability. While early word embeddings exhibited intuitive vector arithmetic (''king'' - ''man'' + ''woman'' = ''queen''), modern high-dimensional text representations lack straightforward…