FaST: Feature-aware Sampling and Tuning for Personalized Preference Alignment with Limited Data
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866913977820250112 |
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| author | Thonet, Thibaut Kruszewski, Germán Rozen, Jos Erbacher, Pierre Dymetman, Marc |
| author_facet | Thonet, Thibaut Kruszewski, Germán Rozen, Jos Erbacher, Pierre Dymetman, Marc |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_04698 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | FaST: Feature-aware Sampling and Tuning for Personalized Preference Alignment with Limited Data Thonet, Thibaut Kruszewski, Germán Rozen, Jos Erbacher, Pierre Dymetman, Marc Computation and Language 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. |
| title | FaST: Feature-aware Sampling and Tuning for Personalized Preference Alignment with Limited Data |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2508.04698 |