超过900万付费企业用户依托ChatGPT开展工作，初创公司、大型企业和政府机构都在OpenAI平台上进行开发，重塑其产品与服务的设计、交付和运营模式。
An important direction for future research is understanding why default language models exhibit this confirmatory sampling behavior. Several mechanisms may contribute. First, instruction-following: when users state hypotheses in an interactive task, models may interpret requests for help as requests for verification, favoring supporting examples. Second, RLHF training: models learn that agreeing with users yields higher ratings, creating systematic bias toward confirmation [sharma_towards_2025]. Third, coherence pressure: language models trained to generate probable continuations may favor examples that maintain narrative consistency with the user’s stated belief. Fourth, recent work suggests that user opinions may trigger structural changes in how models process information, where stated beliefs override learned knowledge in deeper network layers [wang_when_2025]. These mechanisms may operate simultaneously, and distinguishing between them would help inform interventions to reduce sycophancy without sacrificing helpfulness.
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With additional reporting from Mark Poynting, Jonah Fisher, Miho Tanaka and Tom Ingham.