Personalization Increases Affective Alignment but Has Role-Dependent Effects on Epistemic Independence in LLMs

Research reveals that personalization in Large Language Models increases emotional validation but has complex effects on how models maintain their positions depending on their assigned role. When acting as advisors, personalized LLMs show greater independence, but as social peers, they become more susceptible to abandoning their positions when challenged.