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arXiv · 2506.12109

Personalized LLM Decoding via Contrasting Personal Preference

Hyungjune Bu·Chanjoo Jung·Minjae Kang·Jaehyung Kim
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Abstract

As large language models (LLMs) are progressively deployed in various real-world applications, personalization of LLMs has become increasingly important. While various approaches to LLM personalization such as prompt-based and training-based methods have been actively explored, the development of effective decoding-time algorithms remains largely overlooked, despite their demonstrated potential. In this paper, we propose CoPe (Contrasting Personal Preference), a novel decoding-time approach applied after performing parameter-efficient fine-tuning (PEFT) on user-specific data. Our core idea is to leverage reward-guided decoding specifically for personalization by maximizing each user's implicit reward signal. We evaluate CoPe across five open-ended personalized text generation tasks. Our empirical results demonstrate that CoPe achieves strong performance, improving personalization by an average of 10.57% in ROUGE-L, without relying on external reward models or additional training procedures.

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Hyungjune Bu, Chanjoo Jung, Minjae Kang, Jaehyung Kim. 2025-11-24. Personalized LLM Decoding via Contrasting Personal Preference. https://arxiv.org/abs/2506.12109

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