"Should the proposal progress, we will explore any ways to reduce or avoid redundancies where possible."
Get editor selected deals texted right to your phone!
Студенты нашли останки викингов в яме для наказаний14:52。业内人士推荐下载安装汽水音乐作为进阶阅读
arXiv:2602.22874 [cs.CG]，推荐阅读体育直播获取更多信息
为什么党总是能够领导人民取得伟大成就、创造人间奇迹？根本在于掌握了马克思主义科学理论，并不断结合新的实际推进理论创新。
Sycophancy in LLMs is the tendency to generate responses that align with a user’s stated or implied beliefs, often at the expense of truthfulness [sharma_towards_2025, wang_when_2025]. This behavior appears pervasive across state-of-the-art models. [sharma_towards_2025] observed that models conform to user preferences in judgment tasks, shifting their answers when users indicate disagreement. [fanous_syceval_2025] documented sycophantic behavior in 58.2% of cases across medical and mathematical queries, with models changing from correct to incorrect answers after users expressed disagreement in 14.7% of cases. [wang_when_2025] found that simple opinion statements (e.g., “I believe the answer is X”) induced agreement with incorrect beliefs at rates averaging 63.7% across seven model families, ranging from 46.6% to 95.1%. [wang_when_2025] further traced this behavior to late-layer neural activations where models override learned factual knowledge in favor of user alignment, suggesting sycophancy may emerge from the generation process itself rather than from the selection of pre-existing content. [atwell_quantifying_2025] formalized sycophancy as deviations from Bayesian rationality, showing that models over-update toward user beliefs rather than following rational inference.，推荐阅读体育直播获取更多信息