@inproceedings{bu-etal-2025-personalized,
    title = "Personalized {LLM} Decoding via Contrasting Personal Preference",
    author = "Bu, Hyungjune  and
      Jung, ChanJoo  and
      Kang, Minjae  and
      Kim, Jaehyung",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.emnlp-main.1723/",
    pages = "33946--33966",
    ISBN = "979-8-89176-332-6",
    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 Contrasting Personal Preference (CoPe), 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."
}