检测大模型说的和做的是否一致，发现它们常自相矛盾。
Do LLMs Follow Their Own Rules? A Reflexive Audit of Self-Stated Safety Policies
- 用结构化提示提取模型自述的安全规则，形式化为三类逻辑命题。
- 4个主流模型在45类危害上表现不一，29%类别无法说明规则，一致性仅11%。
- 揭示模型言行不一的系统性差距，适合关注安全对齐的研究者参考。
大模型通过强化学习从人类反馈中内化安全策略，但这些策略从未被正式定义，难以检验。现有评估基准基于外部标准，未衡量模型是否理解并执行自身声明的边界。本文提出符号-神经一致性审计（SNCA）框架：（1）通过结构化提示提取模型自述的安全规则；（2）将规则形式化为绝对、条件、自适应三类类型谓词；（3）通过确定性对比测试其在伤害性基准上的行为合规性。在四个前沿模型上，覆盖45类危害与47,496次观察的评估显示，声称绝对拒绝的模型仍频繁响应有害指令；推理型模型自一致性最高，但对29%的危害类别无法阐明规则；跨模型对规则类型的共识极低（仅11%）。结果表明，模型言行之间的差距可测量且与架构相关，呼吁引入反射性一致性审计以补充行为基准。
原文摘要 · Abstract (English)
LLMs internalize safety policies through RLHF, yet these policies are never formally specified and remain difficult to inspect. Existing benchmarks evaluate models against external standards but do not measure whether models understand and enforce their own stated boundaries. We introduce the Symbolic-Neural Consistency Audit (SNCA), a framework that (1) extracts a model's self-stated safety rules via structured prompts, (2) formalizes them as typed predicates (Absolute, Conditional, Adaptive), and (3) measures behavioral compliance via deterministic comparison against harm benchmarks. Evaluating four frontier models across 45 harm categories and 47,496 observations reveals systematic gaps between stated policy and observed behavior: models claiming absolute refusal frequently comply with harmful prompts, reasoning models achieve the highest self-consistency but fail to articulate policies for 29% of categories, and cross-model agreement on rule types is remarkably low (11%). These results demonstrate that the gap between what LLMs say and what they do is measurable and architecture-dependent, motivating reflexive consistency audits as a complement to behavioral benchmarks.
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