Unverified50% confidenceFactExact time
大语言模型的指令跟随能力是通过RLHF训练习得的统计倾向，注意力机制无法从架构层面区分硬规则与软建议
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50%
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Long-term
Relevance
7/17/2026
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Verified大语言模型在RLHF训练阶段，因人类标注员倾向于给更详细、更礼貌的回答打高分，导致模型学会了过度冗长的表达模式83% similarUnverified不同大语言模型因训练数据分布、RLHF偏好对齐策略和模型架构差异，在同一问题上呈现风格迥异的回应倾向82% similarUnverified模型在未收到明确指令时输出图片，意味着系统提示词设计或RLHF训练中存在激励偏差80% similarUnverifiedResearchers have attempted to improve models' pragmatic understanding through instruction tuning and reinforcement learning from human feedback (RLHF), but in essence this amounts to layering heuristic rules on top of statistical models.80% similarUnverified提示词与模型版本存在深度绑定关系，不同版本模型在RLHF训练数据、指令微调策略上的差异会导致相同提示词产生不同输出，这种现象被称为「提示词漂移」77% similar
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