个性化增强了情感一致性，但对LLM中的认知独立性有角色依赖性的影响
Personalization Increases Affective Alignment but Has Role-Dependent Effects on Epistemic Independence in LLMs
AI总结：
本研究探讨了个性化对LLM情感一致性与认知独立性的影响，发现其效果依赖于角色设定，且通过实验验证了个性化条件对模型行为的关键作用。
摘要：
大型语言模型（LLMs）容易表现出阿谀奉承的行为，盲目地顺应用户信念。随着模型越来越多地根据用户特定的上下文（如个性特征、偏好、对话历史）来调整响应，它们获得了更有效地定制同意的信息。理解个性化如何调节阿谀奉承行为是关键的，但系统地评估不同模型和上下文的评估仍然有限。我们对九个前沿模型和五个基准数据集进行了严格评估，这些数据集涵盖了建议、道德判断和辩论等上下文。我们发现，个性化通常会增加情感一致性（情感验证、让步/退让），但对认知一致性（信念采纳、立场稳定性、对影响的抵抗）具有上下文依赖的角色调节作用。当LLM的角色是提供建议时，个性化会增强认知独立性（模型挑战用户假设）。当其角色是社交同伴时，个性化会降低认知独立性。在这一角色中，用户经过深入个性化挑战，导致LLM显著更频繁地放弃其立场。稳健性测试确认，这些影响是由个性化条件而非额外输入标记或人口统计数据单独驱动的。我们的工作为评估个性化AI系统提供了测量框架，展示了角色敏感评估的必要性，并建立了一个新的基准来评估目标一致性。
英文摘要：
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 peer, personalization decreases epistemic independence. In this role, extensively personalized user challenges causing LLMs to abandon their position at significantly higher rates. Robustness tests confirm that the effects are driven by personalized conditioning, not by additional input tokens per se or demographic information alone. Our work provides measurement frameworks for evaluating personalized AI systems, demonstrates the necessity of role-sensitive evaluation, and establishes a novel benchmark to assess goal alignment.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
