Personalized LLM Decoding via Contrasting Personal Preference

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. 1 * Equal contribution (listed in alphabetical order). 1 Code is available at https://github.com/ cleverscent/CoPe .