5 papers
Navigating User Behavior toward Personalized Multimodal Generation
Hengji Zhou, Yufeng Liu, Ye Liu +3
Modern AIGC pipelines deliver high-fidelity images and videos but presuppose a well-formed creation instruction, while end users rarely articulate visual details, leaving generator…
TailorMind: Towards Preference-Aligned Multimodal Content Generation
Hengji Zhou, Ye Liu, Yufeng Liu +3
Personalized content systems depend on available UGC and struggle when suitable content is absent, delayed, or costly to create. Although multimodal generators can synthesize conte…
Low-Rank Contextual Reinforcement Learning from Heterogeneous Human Feedback
Seong Jin Lee, Will Wei Sun, Yufeng Liu
Reinforcement learning from human feedback (RLHF) has become a cornerstone for aligning large language models with human preferences. However, the heterogeneity of human feedback,…
Low-Rank Online Dynamic Assortment with Dual Contextual Information
Seong Jin Lee, Will Wei Sun, Yufeng Liu
As e-commerce expands, delivering real-time personalized recommendations from vast catalogs poses a critical challenge for retail platforms. Maximizing revenue requires careful con…
Prompt-Dependent Ranking of Large Language Models with Uncertainty Quantification
Angel Rodrigo Avelar Menendez, Yufeng Liu, Xiaowu Dai
Rankings derived from pairwise comparisons are central to many economic and computational systems. In the context of large language models (LLMs), rankings are typically constructe…