2025
FaST: Feature-aware Sampling and Tuning for Personalized Preference Alignment with Limited Data
EMNLP 2025
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 t