让新兴产业“新树扎深根”，要点在“强”。我国传统产业规模庞大、门类齐全、基础雄厚，是产业体系的“基本盘”，既为新兴产业发展提供了产业链供应链支撑，又为新兴产业提供了巨大的市场需求。对此，我们要坚定不移推动传统产业“焕新”，运用数智技术、绿色技术改造提升传统产业，增强全产业链竞争优势。
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.
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