Finding topics to write about can be a mentally demanding process. However, topic hierarchies can help writers explore topics of varying levels of specificity. In this paper, we use large language models (LLMs) to help construct topic hierarchies. Although LLMs have access to such knowledge, it can be difficult to elicit due to issues of specificity, scope, and repetition. We designed and tested three different prompting techniques to find one that maximized accuracy. We found that prepending the general topic area to a prompt yielded the most accurate results with 85% accuracy. We discuss applications of this research including STEM writing, education, and content creation.

翻译：寻找写作主题可能是一个耗费脑力的过程。然而，主题层次结构可以帮助作者探索不同具体程度的话题。在本文中，我们利用大型语言模型来协助构建主题层次结构。尽管大型语言模型具备此类知识，但由于具体性、范围以及重复性问题，提取这些知识可能较为困难。我们设计并测试了三种不同的提示技术，以找到一种能最大化准确性的方法。研究发现，在提示前附加通用主题领域能获得最准确的结果，准确率达到85%。我们讨论了这项研究的应用，包括STEM写作、教育和内容创作。