CLIPer: Tailoring Diverse User Preference via Classifier-Guided Inference-Time Personalization

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 introduce $\mathbf{\text{CLIPer}}$($\mathbf{\text{Cl}}$assifier-guided $\mathbf{\text{I}}$nference-time $\mathbf{\text{Per}}$sonalization), a lightweight personalization approach that leverages a classifier model to steer LLM generation dynamically to different user preferences at inference time. Our method eliminates the need for extensive fine-tuning, inducing negligible additional computational overhead while enabling more controllable and nuanced personalization across single and multi-dimensional preferences. Comprehensive empirical analyses demonstrate the scalability and effectiveness of our approach in delivering personalized language generation.