AI RESEARCH
Preference-Aware Rubric Learning for Personalized Evaluation
arXiv CS.CL
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ArXi:2605.31545v1 Announce Type: new As Large Language Models (LLMs) evolve from general-purpose assistants to user-centric agents, personalization has become central to aligning model behavior with individual preferences, making the evaluation of personalized alignment a critical bottleneck. Existing evaluation methods-ranging from automatic metrics to LLM-as-a-judge approaches-fail to capture subjective, user-specific preferences embedded in long-term interaction histories.