@inproceedings{thonet-etal-2025-fast,
    title = "{F}a{ST}: Feature-aware Sampling and Tuning for Personalized Preference Alignment with Limited Data",
    author = "Thonet, Thibaut  and
      Kruszewski, Germ{\'a}n  and
      Rozen, Jos  and
      Erbacher, Pierre  and
      Dymetman, Marc",
    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.475/",
    pages = "9352--9381",
    ISBN = "979-8-89176-332-6",
    abstract = "LLM-powered conversational assistants are often deployed in a one-size-fits-all manner, which fails to accommodate individual user preferences. Recently, LLM personalization {--} tailoring models to align with specific user preferences {--} has gained increasing attention as a way to bridge this gap. In this work, we specifically focus on a practical yet challenging setting where only a small set of preference annotations can be collected per user {--} a problem we define as Personalized Preference Alignment with Limited Data (PPALLI). To support research in this area, we introduce two datasets {--} DnD and ELIP {--} and benchmark a variety of alignment techniques on them. We further propose FaST, a highly parameter-efficient approach that leverages high-level features automatically discovered from the data, achieving the best overall performance."
}