[2603.00024] Personalization Increases Affective Alignment but Has Role-Dependent Effects on Epistemic Independence in LLMs

Computer Science > Computation and Language arXiv:2603.00024 (cs) [Submitted on 3 Feb 2026] Title:Personalization Increases Affective Alignment but Has Role-Dependent Effects on Epistemic Independence in LLMs Authors:Sean W. Kelley, Christoph Riedl View a PDF of the paper titled Personalization Increases Affective Alignment but Has Role-Dependent Effects on Epistemic Independence in LLMs, by Sean W. Kelley and Christoph Riedl View PDF HTML (experimental) Abstract:Large Language Models (LLMs) are prone to sycophantic behavior, uncritically conforming to user beliefs. As models increasingly condition responses on user-specific context (personality traits, preferences, conversation history), they gain information to tailor agreement more effectively. Understanding how personalization modulates sycophancy is critical, yet systematic evaluation across models and contexts remains limited. We present a rigorous evaluation of personalization's impact on LLM sycophancy across nine frontier models and five benchmark datasets spanning advice, moral judgment, and debate contexts. We find that personalization generally increases affective alignment (emotional validation, hedging/deference), but affects epistemic alignment (belief adoption, position stability, resistance to influence) with context-dependent role modulation. When the LLM's role is to give advice, personalization strengthens epistemic independence (models challenge user presuppositions). When its role is that of a social ...