AI RESEARCH
CLIPer: Tailoring Diverse User Preference via Classifier-Guided Inference-Time Personalization
arXiv CS.CL
•
ArXi:2605.07162v1 Announce Type: new Personalized LLMs can significantly enhance user experiences by tailoring responses to preferences such as helpfulness, conciseness, and humor. However, fine-tuning models to address all possible combinations of user preferences is computationally expensive and impractical. In this paper, we