CLIPer：通过分类器引导的推理时个性化实现多样化用户偏好
CLIPer: Tailoring Diverse User Preference via Classifier-Guided Inference-Time Personalization
AI总结：
CLIPer通过分类器引导在推理时个性化，实现多样化用户偏好，无需大量微调，具有高效可控的个性化能力。
摘要：
个性化大语言模型可以通过调整响应以适应用户偏好如有用性、简洁性和幽默感来显著提升用户体验。然而，对所有可能的用户偏好组合进行微调计算成本高且不现实。本文介绍CLIPer（分类器引导的推理时个性化），一种轻量级的个性化方法，利用分类器模型在推理时动态引导LLM生成以适应不同用户偏好。我们的方法消除了大量微调的需求，诱导极小的额外计算开销，同时在单维和多维偏好上实现更可控和细致的个性化。全面的实证分析展示了我们的方法在交付个性化语言生成方面的可扩展性和有效性。
英文摘要：
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 \textbf{CLIPer}(\textbf{Cl}assifier-guided \textbf{I}nference-time \textbf{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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
