AI 猜谜语，62 种文化里最高只对 22%
谜语是文化的压缩包：一句谜面里藏着物产、传说、双关和当地人的联想方式。研究者发动 62 个文化社区(45 国、51 种语言)的人亲手写谜语，再拿去考 28 个主流大模型。结果：最强模型在最擅长的社区里也只答对 22%，多数社区远低于此。这不是模型笨，而是它学到的「世界知识」大多来自英文语料，换一种语言、换一套隐喻，它就抓瞎。它不是你明天能用上的东西，但它给了个诚实的尺子：AI 的「懂文化」，离人类还差着 78%。
📄 原文摘要(英文)
As LLMs expand their language coverage, evaluating how well they understand and represent diverse cultures becomes increasingly important. However, many benchmarks remain English-centric, with limited cultural coverage and a focus on factual knowledge over its application in real-world cultural contexts. We propose CulturalRiddles, a benchmark of original, culturally grounded riddles created entirely by humans through a participatory community effort, spanning 62 cultures (45 countries, 51 languages). These riddles are deeply rooted in their respective cultures and require abductive and compositional reasoning, alongside detailed factual and social knowledge, language skills, and an understanding of culturally specific associations. We benchmark 28 LLM configurations using both automatic and large-scale human evaluation. Even frontier models achieve at most 22% accuracy in some communities. We publicly release all artifacts alongside an open contribution process to expand the benchmark's linguistic and cultural coverage.