AIBearisharXiv – CS AI · 5h ago7/10
🧠
Re-Centering Humans in LLM Personalization
Researchers reveal a significant gap between synthetic and real-world performance in LLM personalization systems by analyzing 550 human conversations across three stages: attribute extraction, attribute selection, and response generation. The study finds that current models struggle with human-aligned personalization and that learned reward models fail to adequately capture human preferences, highlighting fundamental limitations in how AI systems understand and incorporate user information.