The authors tackle collective decision-making over free-text opinions by building embeddings that capture preferential similarity (does someone agree with this text?) rather than semantic similarity (does this text mean the same thing?). Standard text embeddings conflate the two because stance and style are correlated in real data, so models can appear to predict preferences even when they're actually relying on irrelevant surface features ("nuisance variables"). The solution is synthetic training data designed to break the correlation between semantic and preferential similarity, which provably shifts the learned geometry away from style-dominated cosine similarity and significantly improves preference prediction across 11 deliberation datasets.
Main takeaways:
- Standard embeddings measure semantic similarity (similar wording) but fail when you need preferential similarity (similar agreement), because stance and style are normally correlated.
- This is formalized as an invariance problem: embeddings encode both preference-relevant signal (stance, values) and semantic nuisance (style, wording), and can look correct when relying on nuisance alone.
- Synthetic training data that decorrelates stance from style provably changes the optimal embedding geometry away from nuisance-dominated metrics.
- The method significantly improves preference prediction on 11 real-world deliberation datasets where semantic and preferential similarity come apart.