Anthropic’s “Towards Understanding Sycophancy in Language Models” (ICLR 2024) paper showed that five state-of-the-art AI assistants exhibited sycophantic behavior across a number of different tasks. When a response matched a user’s expectation, it was more likely to be preferred by human evaluators. The models trained on this feedback learned to reward agreement over correctness.
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通过架构设计实现模型间制衡，为治理人工智能幻觉提供了结构性解决方案。这种跨模型校验机制比单一模型的自我修正更为可靠。