arXiv (All)
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Collaborative Personalized Preference Alignment for LLMs under Data Deficiency
Real-world users often exhibit highly heterogeneous preferences over multiple objectives for LLM responses. A lightweight aligner can tailor these responses to individual preferences, but scarce user-specific feedback makes personalized training difficult. Learning shared initializations across user
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