这篇用探针方法拆开了LLM一本正经胡说八道的原因：模型内部其实知道自己不知道，但嘴硬不肯说泛话。
这篇论文受格莱斯合作原则启发，提出LLM在遇到未知实体时应从具体表述撤退到更安全的泛化表述。作者基于T-REx构建基准，改变实体熟悉度和指称特异性，探测模型内部激活是否编码了知识边界和待生成指称的特异性。结果显示两种信号都存在，但生成时模型仍偏好具体指称，即使提供正确的泛化选项也如此。这表明实现Gricean退避所需的基本表征已具备，但缺少将其转化为生成策略的机制。
Toward a Gricean Retreat: Probing LLMs for Knowledge Boundaries and Referent Specificity
When asked about entities outside their knowledge boundary, LLMs routinely fabricate plausible-sounding details rather than backing off to safer, more general claims. We frame this failure through a Gricean lens: a cooperative speaker who is uncertain about a referent retreats up the specificity hierarchy, trading informativeness for truthfulness. We ask whether LLMs have the ingredients to perform this retreat. Using a T-REx-based benchmark that varies entity familiarity and referent specificity, we probe models to answer two questions: (i) do their activations encode whether a referent falls inside the knowledge boundary, and (ii) do they anticipate the specificity of the referent they are about to generate? We find that the answer to both is yes, but the two signals are not reconciled in generation. Models overwhelmingly prefer specific referents even when the entity is unknown to them, and do so even when offered correct generic alternatives. The substrate for a Gricean retreat is present, but the policy that would act on it is not. We position our findings as a first step toward Gricean alignment, training or steering objectives that couple knowledge-boundary awareness to referent-specificity during generation.