Rhetorical questions are asked not to seek information but to persuade or signal stance. How large language models internally represent them remains unclear. We analyze rhetorical questions in LLM representations using linear probes on two social-media datasets with different discourse contexts, and find that rhetorical signals emerge early and are most stably captured by last-token representations. Rhetorical questions are linearly separable from information-seeking questions within datasets, and remain detectable under cross-dataset transfer, reaching AUROC around 0.7-0.8. However, we demonstrate that transferability does not simply imply a shared representation. Probes trained on different datasets produce different rankings when applied to the same target corpus, with overlap among the top-ranked instances often below 0.2. Qualitative analysis shows that these divergences correspond to distinct rhetorical phenomena: some probes capture discourse-level rhetorical stance embedded in extended argumentation, while others emphasize localized, syntax-driven interrogative acts. Together, these findings suggest that rhetorical questions in LLM representations are encoded by multiple linear directions emphasizing different cues, rather than a single shared direction.

翻译：修辞疑问句的提出并非为获取信息，而是为了说服或表达立场。大型语言模型如何在内部表征它们仍不清楚。我们使用线性探针，在两个具有不同话语情境的社交媒体数据集上分析了LLM表征中的修辞疑问句，并发现修辞信号在早期阶段就已显现，且最稳定地被最后一个词元的表征所捕获。在数据集内部，修辞疑问句与信息寻求疑问句是线性可分的，并且在跨数据集迁移下仍可检测，AUROC达到约0.7-0.8。然而，我们证明了迁移性并不简单意味着存在共享的表征。当应用于同一目标语料库时，在不同数据集上训练的探针会产生不同的排名，排名靠前实例的重叠率通常低于0.2。定性分析表明，这些差异对应于不同的修辞现象：一些探针捕捉嵌入在扩展论证中的话语层面的修辞立场，而其他探针则强调局部的、句法驱动的疑问行为。总之，这些发现表明，LLM表征中的修辞疑问句是由多个强调不同线索的线性方向编码的，而非单一共享方向。