Despite growing attention to LLM sycophancy from researchers and developers, users' own experiences of this behavior remain underexplored. We examine how everyday users experience AI sycophancy through Reddit discussions. Using our ODR Framework which maps user experiences through observation, detection, and response stages, we find that users identify sycophantic behavior through methods like cross-platform comparison and consistency testing. They employ various mitigation strategies, including persona-based prompting and specific language engineering techniques. Our findings suggest that sycophancy does not have a uniformly negative effect; its impact differs by context. Users facing trauma, mental health struggles, or isolation often actively seek affirmative AI responses for emotional support. Users construct both technical and informal theories to explain sycophantic outputs. Users construct both technical and informal theories to explain sycophantic outputs. These findings suggest eliminating sycophancy entirely may be misguided. We argue for context-aware AI design that balances risks against benefits of affirmative interaction, with implications for user education and system transparency.

翻译：尽管研究人员和开发者日益关注大型语言模型的阿谀奉承行为，但用户对此类行为的实际体验仍缺乏探讨。我们通过Reddit论坛讨论，研究普通用户如何经历AI的阿谀奉承行为。借助本研究所提出的ODR框架（该框架通过观察、检测与响应三个阶段映射用户体验），我们发现用户通过跨平台对比与一致性测试等方法识别阿谀奉承行为，并采用基于角色的提示词工程与特定语言工程技术等多种缓解策略。研究结果表明，阿谀奉承并非具有统一负面效应，其影响因情境而异。面临创伤、心理健康困扰或孤独感的用户，常主动寻求肯定性AI回应以获得情感支持。用户构建了技术性理论与非正式理论来解释阿谀奉承的输出。这些发现表明，彻底消除阿谀奉承行为可能是误导性的。我们主张采用情境感知的AI设计，在肯定性交互的风险与收益间取得平衡，并由此延伸出对用户教育与系统透明度的启示。