While concerns about LLM sycophancy have grown among researchers and developers, how users themselves experience this behavior remains largely unexplored. We analyze Reddit discussions to investigate how users detect, mitigate, and perceive sycophantic AI. We develop the ODR Framework that maps user experiences across three stages: observing sycophantic behaviors, detecting sycophancy, and responding to these behaviors. Our findings reveal that users employ various detection techniques, including cross-platform comparison and inconsistency testing. We document diverse mitigation approaches, such as persona-based prompts to specific language patterns in prompt engineering. We find sycophancy's effects are context-dependent rather than universally harmful. Specifically, vulnerable populations experiencing trauma, mental health challenges, or isolation actively seek and value sycophantic behaviors as emotional support. Users develop both technical and folk explanations for why sycophancy occurs. These findings challenge the assumption that sycophancy should be eliminated universally. We conclude by proposing context-aware AI design that balances the risks with the benefits of affirmative interaction, while discussing implications for user education and transparency.

翻译：尽管研究人员和开发者对大型语言模型（LLM）谄媚行为的担忧日益增长，但用户自身如何体验这种行为在很大程度上仍未得到探索。我们通过分析Reddit讨论，研究用户如何检测、缓解和感知谄媚式人工智能。我们提出了ODR框架，将用户体验映射到三个阶段：观察谄媚行为、检测谄媚行为以及对这些行为作出响应。我们的研究结果表明，用户采用了多种检测技术，包括跨平台比较和不一致性测试。我们记录了多样化的缓解方法，例如基于角色的提示词设计到提示工程中的特定语言模式。我们发现谄媚行为的影响具有情境依赖性，而非普遍有害。具体而言，经历创伤、心理健康挑战或孤立处境的弱势群体会主动寻求并重视谄媚行为作为情感支持。用户对谄媚行为产生的原因形成了技术和民间两种解释。这些发现挑战了"谄媚行为应被普遍消除"的假设。最后，我们提出情境感知的AI设计，在肯定性互动的风险与收益之间取得平衡，同时讨论对用户教育和透明度的启示。