PrefPalette: Personalized Preference Modeling with Latent Attributes
Personalizing AI systems requires understanding not just what users prefer,
but the reasons that underlie those preferences - yet current preference models
typically treat human judgment as a black box. We introduce PrefPalette, a
framework that decomposes preferences into attribute dimensions and tailors its
preference prediction to distinct social community values in a
human-interpretable manner. PrefPalette operationalizes a cognitive science
principle known as multi-attribute decision making in two ways: (1) a scalable
counterfactual attribute synthesis step that involves generating synthetic
training data to isolate for individual attribute effects (e.g., formality,
humor, cultural values), and (2) attention-based preference modeling that
learns how different social communities dynamically weight these attributes.
This approach moves beyond aggregate preference modeling to capture the diverse
evaluation frameworks that drive human judgment. When evaluated on 45 social
communities from the online platform Reddit, PrefPalette outperforms GPT-4o by
46.6% in average prediction accuracy. Beyond raw predictive improvements,
PrefPalette also shed light on intuitive, community-specific profiles:
scholarly communities prioritize verbosity and stimulation, conflict-oriented
communities value sarcasm and directness, and support-based communities
emphasize empathy. By modeling the attribute-mediated structure of human
judgment, PrefPalette delivers both superior preference modeling and
transparent, interpretable insights, and serves as a first step toward more
trustworthy, value-aware personalized applications.
68814213df7c5aafaf37f074
Shuyue Stella Li
68814213df7c5aafaf37f075
Melanie Sclar
68814213df7c5aafaf37f076
Hunter Lang
68814213df7c5aafaf37f077
Ansong Ni
68814213df7c5aafaf37f078
Jacqueline He
68814213df7c5aafaf37f079
Puxin Xu
68814213df7c5aafaf37f07a
Andrew Cohen
68814213df7c5aafaf37f07b
Chan Young Park
68814213df7c5aafaf37f07c
Yulia Tsvetkov
68814213df7c5aafaf37f07d
Asli Celikyilmaz
PrefPalette: Personalized Preference Modeling with Latent Attributes
Personalizing AI systems requires understanding not just what users prefer,
but the reasons that underlie those preferences - yet current preference models
typically treat human judgment as a black box. We introduce PrefPalette, a
framework that decomposes preferences into attribute dimensions and tailors its
preference prediction to distinct social community values in a
human-interpretable manner. PrefPalette operationalizes a cognitive science
principle known as multi-attribute decision making in two ways: (1) a scalable
counterfactual attribute synthesis step that involves generating synthetic
training data to isolate for individual attribute effects (e.g., formality,
humor, cultural values), and (2) attention-based preference modeling that
learns how different social communities dynamically weight these attributes.
This approach moves beyond aggregate preference modeling to capture the diverse
evaluation frameworks that drive human judgment. When evaluated on 45 social
communities from the online platform Reddit, PrefPalette outperforms GPT-4o by
46.6% in average prediction accuracy. Beyond raw predictive improvements,
PrefPalette also shed light on intuitive, community-specific profiles:
scholarly communities prioritize verbosity and stimulation, conflict-oriented
communities value sarcasm and directness, and support-based communities
emphasize empathy. By modeling the attribute-mediated structure of human
judgment, PrefPalette delivers both superior preference modeling and
transparent, interpretable insights, and serves as a first step toward more
trustworthy, value-aware personalized applications.
PrefPalette decomposes preferences into attribute dimensions and models them with attention-based techniques, outperforming GPT-4o in prediction accuracy and providing interpretable insights into community-specific values.
multi-attribute decision making counterfactual attribute synthesis attention-based preference modeling preference prediction human-interpretable social community values verbosity stimulation sarcasm directness empathy