Few-Shot Preference Optimization (FSPO) personalizes LLMs by reframing reward modeling as a meta-learning problem, enabling rapid adaptation to user preferences with minimal labeled data, leveraging synthetic datasets for scalability, and achieving high success rates in personalized content generation across multiple domains.
☆17Feb 27, 2025Updated last year
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