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This position paper argues that large language models should learn from personalized rather than aggregated human preferences, highlighting theoretical limitations from social choice theory and practical issues from demographic diversity. It proposes bounded personalization frameworks that respect individual autonomy while maintaining universal safety constraints.
Introduces a stratified framework to identify the minimal aggregated preference information needed to compute disagreement measures, proposing the plurality matrix and showing that pairwise comparisons are insufficient; designs elicitation protocols that trade off participant numbers and cognitive load.
This paper introduces a new embedding model designed to capture preferential similarity rather than just semantic similarity, improving preference prediction for collective decision-making systems.