
Embeddings for Preferences, Not Semantics
Researchers introduce embeddings optimized for preferential similarity in collective decision-making from free-form text opinions. Standard semantic embeddings fail when preference-semantic correlations break due to nuisance factors like style. Synthetic training data provably improves preference prediction across 11 online deliberation datasets.





