FaST: Feature-aware Sampling and Tuning for Personalized Preference Alignment with Limited Data
Thibaut Thonet, Germ{\'a}n Kruszewski, Jos Rozen, Pierre Erbacher, Marc Dymetman
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.
BibTeX
@inproceedings{emnlp2025_fastfeatureaware,
title = {FaST: Feature-aware Sampling and Tuning for Personalized Preference Alignment with Limited Data},
author = {Thibaut Thonet and Germ{\'a}n Kruszewski and Jos Rozen and Pierre Erbacher and Marc Dymetman},
booktitle = {EMNLP 2025},
year = {2025}
}