We study dictionary definition generation (DDG), i.e., the generation of non-contextualized definitions for given headwords. Dictionary definitions are an essential resource for learning word senses, but manually creating them is costly, which motivates us to automate the process. Specifically, we address learner's dictionary definition generation (LDDG), where definitions should consist of simple words. First, we introduce a reliable evaluation approach for DDG, based on our new evaluation criteria and powered by an LLM-as-a-judge. To provide reference definitions for the evaluation, we also construct a Japanese dataset in collaboration with a professional lexicographer. Validation results demonstrate that our evaluation approach agrees reasonably well with human annotators. Second, we propose an LDDG approach via iterative simplification with an LLM. Experimental results indicate that definitions generated by our approach achieve high scores on our criteria while maintaining lexical simplicity.

翻译：本研究探讨词典定义生成（DDG），即针对给定词目生成非语境化定义的任务。词典定义是学习词义的重要资源，但其人工编纂成本高昂，这促使我们探索自动化生成方法。具体而言，我们聚焦于学习者词典定义生成（LDDG），其定义需由简单词汇构成。首先，我们基于新设计的评估标准，提出一种由大语言模型作为评判者的可靠DDG评估方法。为提供评估所需的参考定义，我们还与专业词典编纂者合作构建了日语数据集。验证结果表明，该评估方法与人工标注者具有良好的一致性。其次，我们提出通过大语言模型迭代简化的LDDG方法。实验结果显示，该方法生成的词条在我们的评估标准中获得高分，同时保持了词汇的简易性。