Crossword puzzles are popular linguistic games often used as tools to engage
students in learning. Educational crosswords are characterized by less cryptic
and more factual clues that distinguish them from traditional crossword
puzzles. Despite there exist several publicly available clue-answer pair
databases for traditional crosswords, educational clue-answer pairs datasets
are missing. In this article, we propose a methodology to build educational
clue generation datasets that can be used to instruct Large Language Models
(LLMs). By gathering from Wikipedia pages informative content associated with
relevant keywords, we use Large Language Models to automatically generate
pedagogical clues related to the given input keyword and its context. With such
an approach, we created clue-instruct, a dataset containing 44,075 unique
examples with text-keyword pairs associated with three distinct crossword
clues. We used clue-instruct to instruct different LLMs to generate educational
clues from a given input content and keyword. Both human and automatic
evaluations confirmed the quality of the generated clues, thus validating the
effectiveness of our approach.

通过从维基百科页面中收集与相关关键词相关的信息内容，利用大型语言模型自动生成与给定输入关键词及其上下文相关的教育线索，我们提出了一种构建教育线索生成数据集的方法，并使用此方法创建了一个包含 44075 个唯一示例的数据集：clue-instruct。我们使用 clue-instruct 来教授不同的大型语言模型从给定的输入内容和关键词中生成教育线索，无论是人工评估还是自动评估，都证实了生成线索的质量，从而验证了我们方法的有效性。