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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国务院总理李强5日在政府工作报告中指出，我们要以铸牢中华民族共同体意识为主线，积极促进各民族广泛交往交流交融，加快民族地区现代化建设步伐，推进中华民族共同体建设。坚持党的宗教工作基本方针，坚持我国宗教中国化方向，加强宗教事务治理法治化。全面贯彻党的侨务政策，维护海外侨胞和归侨侨眷合法权益，更好凝聚侨心侨力。