Papers by Pierre Erbacher
FaST: Feature-aware Sampling and Tuning for Personalized Preference Alignment with Limited Data (2025.emnlp-main)
Copied to clipboard
| Challenge: | Recent studies have focused on personalizing conversational assistants to meet specific user preferences. |
| Approach: | They propose to use a dataset to analyze a problem where only a small set of preference annotations can be collected per user. |
| Outcome: | The proposed approach leverages high-level features discovered from the data, achieving the best overall performance. |