@inproceedings{zhang-etal-2025-personalize,
    title = "Personalize Your {LLM}: Fake it then Align it",
    author = "Zhang, Yijing  and
      Adila, Dyah  and
      Shin, Changho  and
      Sala, Frederic",
    editor = "Chiruzzo, Luis  and
      Ritter, Alan  and
      Wang, Lu",
    booktitle = "Findings of the Association for Computational Linguistics: NAACL 2025",
    month = apr,
    year = "2025",
    address = "Albuquerque, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2025.findings-naacl.407/",
    pages = "7287--7301",
    ISBN = "979-8-89176-195-7",
    abstract = "Personalizing large language models (LLMs) is essential for delivering tailored interactions that improve user experience. Many existing personalization methods require fine-tuning LLMs for each user, rendering them prohibitively expensive for widespread adoption. Although retrieval-based approaches offer a more compute-efficient alternative, they still depend on large, high-quality datasets that are not consistently available for all users. To address this challenge, we propose Chameleon, a scalable and efficient personalization approach that uses (1) self-generated personal preference data and (2) representation editing to enable quick and cost-effective personalization. Our experiments on various tasks, including those from the LaMP personalization benchmark, show that Chameleon efficiently adapts models to personal preferences, improving instruction-tuned models and outperforms two personalization baselines by an average of 40{\%} across two model architectures."
}