CAGE uses common-agency games and an EPEC algorithm to compute equilibrium policies that balance multiple conflicting objectives for test-time LLM alignment.
Direct alignment with heterogeneous preferences
4 Pith papers cite this work. Polarity classification is still indexing.
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PAFO applies Pareto fairness optimization and group-specialized distillation to produce a single personalized reward model that improves accuracy for both majority and minority preference groups without requiring group labels at inference.
FedVPA-GP applies variational preference learning in a federated setting with a mixture prior and orthogonal loss to disentangle user preferences on the HH-RLHF dataset.
A tradeoff model shows generative AI can reduce bias against diverse preferences by strategically eliciting information instead of always inferring from majority patterns.
citing papers explorer
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Common-agency Games for Multi-Objective Test-Time Alignment
CAGE uses common-agency games and an EPEC algorithm to compute equilibrium policies that balance multiple conflicting objectives for test-time LLM alignment.
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PAFO: Pareto Fairness Optimization for Personalized Reward Modeling
PAFO applies Pareto fairness optimization and group-specialized distillation to produce a single personalized reward model that improves accuracy for both majority and minority preference groups without requiring group labels at inference.
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Federated Variational Preference Alignment with Gumbel-Softmax Prior for Personalized User Preferences
FedVPA-GP applies variational preference learning in a federated setting with a mixture prior and orthogonal loss to disentangle user preferences on the HH-RLHF dataset.
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When to Ask a Question: Understanding Communication Strategies in Generative AI Tools
A tradeoff model shows generative AI can reduce bias against diverse preferences by strategically eliciting information instead of always inferring from majority patterns.