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cs.AI2026
Gradient-Aligned Pair Selection for Personalized Preference Optimization
Ruoming Jin, Xinyu Li, Hao Zhou +6
Personalizing large language models (LLMs) requires aligning generation behavior with user-specific preferences rather than aggregate quality. While Direct Preference Optimization…
cs.AI2026
Aplaud: Adaptive Personalized Low-Rank Decomposition for User-Specific LLM
Xinyu Li, Ruoming Jin, Jianfeng Zhu +2
In this paper, we study the problem of personalized survey response prediction using fine-tuned large language models (LLMs). This task poses unique challenges: limited per-user tr…
cs.AI2026
PLUME: Parameter-Efficient Personalization of Large Language Models via Low-Rank User Modulation in Shared Subspaces
Xinyu Li, Hao Zhou, Jianfeng Zhu +4
Personalizing large language models (LLMs) is essential for delivering AI assistance that aligns with individual users' styles, intents, and preferences. While per-user fine-tuning…