Pop culture is an important aspect of communication. On social media people often post pop culture reference images that connect an event, product or other entity to a pop culture domain. Creating these images is a creative challenge that requires finding a conceptual connection between the users' topic and a pop culture domain. In cognitive theory, this task is called conceptual blending. We present a system called PopBlends that automatically suggests conceptual blends. The system explores three approaches that involve both traditional knowledge extraction methods and large language models. Our annotation study shows that all three methods provide connections with similar accuracy, but with very different characteristics. Our user study shows that people found twice as many blend suggestions as they did without the system, and with half the mental demand. We discuss the advantages of combining large language models with knowledge bases for supporting divergent and convergent thinking.

翻译：流行文化是交流的重要方面。在社交媒体上，人们常发布能连接事件、产品或其他实体至某一流行文化领域的流行文化参考图像。创作这些图像是一项需要寻找用户主题与流行文化领域之间概念联系的创造性挑战。在认知理论中，这一任务被称为概念融合。我们提出了一种名为PopBlends的系统，能够自动建议概念融合。该系统探索了三种方法，包括传统知识提取方法与大型语言模型。我们的标注研究表明，三种方法在准确性上提供相似的连接，但具有截然不同的特征。用户研究显示，使用系统后，人们发现的概念融合建议数量是不使用系统时的两倍，且脑力需求减半。我们讨论了将大型语言模型与知识库结合以支持发散与收敛思维的优势。