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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很多人将新闻专业主义，刻板地理解为理性、中立、客观。但在不久的将来，AI 绝对可以写出比人类中立一万倍、四平八稳绝对不会犯错的“八股文”。真正的新闻专业主义，恰恰是 AI 做不到的那些部分：
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