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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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圖像加註文字，德黑蘭居民報告稱，週一和週二該城市仍繼續受轟炸（3月3日攝於德黑蘭的資料照片）。Article InformationAuthor, 貢切赫·哈比比亞扎德 (Ghoncheh Habibiazad)
President Donald Trump has been critical of the UK for not doing more in the conflict with Iran
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“美, 하메네이처럼 김정은 제거 어렵다…北, 한국에 핵무기 쏠 위험”，这一点在体育直播中也有详细论述
23:31, 4 марта 2026Мир