Embeddings for Preferences, Not Semantics

This paper argues that for collective decision-making based on free-form text, embeddings should measure "preferential similarity" rather than "semantic similarity". Existing embeddings capture a coarse preference signal, but fail when this correlation breaks, a problem formalized as an invariance issue.