Riddles are concise linguistic puzzles that describe an object or idea through indirect, figurative, or playful clues. They are a longstanding form of creative expression, requiring the solver to interpret hints, recognize patterns, and draw inferences to identify the answers. In this work, we introduce a simple pipeline for creating and evaluating analogy-based riddles. The system includes a triples creator that builds structured facts about a concept, a semantic mapper that selects attributes useful for analogy, a stylized generator that turns them into riddle clues, and a validator that collects all possible answers the riddle could point to. We use this validator to study whether large language models can recover the full answer set for different riddle types. Our case study shows that while models often guess the main intended answer, they frequently miss other valid interpretations. This highlights the value of riddles as a lightweight tool for examining reasoning coverage and ambiguity handling in language models.

翻译：谜语是一种简洁的语言谜题，通过间接、比喻或俏皮的线索描述某个物体或概念。作为一种历史悠久的创造性表达形式，它要求解谜者解读提示、识别模式并进行推理以确定答案。在本研究中，我们提出了一种用于创建和评估基于类比的谜语的简易流程。该系统包含：三元组生成器（构建关于概念的结构化事实）、语义映射器（选择适用于类比的属性）、风格化生成器（将属性转化为谜语线索）以及验证器（收集谜语可能指向的所有潜在答案）。我们利用该验证器研究大型语言模型是否能还原不同谜语类型的完整答案集合。案例研究表明，虽然模型常能猜出主要预期答案，但经常遗漏其他有效解释。这凸显了谜语作为一种轻量级工具的价值，可用于检验语言模型的推理覆盖范围与歧义处理能力。