Утро жителей Харькова началось со взрывов08:46
"Just weeks ago, we called out abhorrent non-consensual intimate images being shared on Grok, which led to functionality being removed, and now ministers are legislating to make 'nudification' tools illegal and bringing additional chatbots within scope of the Online Safety Act."
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В октябре 2025 года Трамп пригрозил Испании «выговором» за отказ увеличить расходы на оборону до пяти процентов ВВП, как это сделали другие страны НАТО.。业内人士推荐heLLoword翻译官方下载作为进阶阅读
11:37, 3 марта 2026Спорт
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.