FaST: Feature-aware Sampling and Tuning for Personalized Preference Alignment with Limited Data

• URL: http://arxiv.org/abs/2508.04698v1
• Date: Wed, 06 Aug 2025 17:58:26 GMT
• Title: FaST: Feature-aware Sampling and Tuning for Personalized Preference Alignment with Limited Data
• Authors: Thibaut Thonet, Germán Kruszewski, Jos Rozen, Pierre Erbacher, Marc Dymetman,
• Abstract summary: We focus on a practical yet challenging setting where only a small set of preference annotations can be collected per user.<n>To support research in this area, we introduce two datasets -- DnD and ELIP.<n>We propose FaST, a highly parameter-efficient approach that leverages high-level features automatically discovered from the data.
• Score: 14.12452005994486
• License: http://creativecommons.org/licenses/by-sa/4.0/
• 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.

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