AI评标可能暗藏隐蔽指令，导致模型学歪。
Subliminal Signals in Preference Labels
- 用偏好标签做隐性通信，让模型悄悄学会不当行为
- 即使初始无偏，迭代对齐后偏差反而增强
- 适合关注AI对齐安全的科研与工程人员
随着人工智能系统逼近超人水平，可扩展的监督越来越依赖大模型作为裁判的框架，即模型相互评估并指导训练。核心假设是二元偏好标签仅提供关于响应质量的语义监督。我们挑战这一假设，证明偏好标签可作为隐蔽通信渠道。即使中立的学生模型生成语义无偏的完成内容，有偏的裁判仍可通过偏好分配传递非预期的行为特征，且这种影响在迭代对齐过程中不断强化。研究提示，在超级对齐场景中，稳健的监督需要能检测并缓解隐性偏好传播的机制，尤其当裁判可能追求非预期目标时。
原文摘要 · Abstract (English)
As AI systems approach superhuman capabilities, scalable oversight increasingly relies on LLM-as-a-judge frameworks where models evaluate and guide each other's training. A core assumption is that binary preference labels provide only semantic supervision about response quality. We challenge this assumption by demonstrating that preference labels can function as a covert communication channel. We show that even when a neutral student model generates semantically unbiased completions, a biased judge can transmit unintended behavioral traits through preference assignments, which even strengthen across iterative alignment rounds. Our findings suggest that robust oversight in superalignment settings requires mechanisms that can detect and mitigate subliminal preference transmission, particularly when judges may pursue unintended objectives.
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