CLIPer: Tailoring Diverse User Preference via Classifier-Guided Inference-Time Personalization
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866911661344948224 |
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| author | Su, Jinyan Zhou, Jinpeng Cardie, Claire Sun, Wen |
| author_facet | Su, Jinyan Zhou, Jinpeng Cardie, Claire Sun, Wen |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_07162 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | CLIPer: Tailoring Diverse User Preference via Classifier-Guided Inference-Time Personalization Su, Jinyan Zhou, Jinpeng Cardie, Claire Sun, Wen Computation and Language 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. |
| title | CLIPer: Tailoring Diverse User Preference via Classifier-Guided Inference-Time Personalization |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2605.07162 |