This article investigates a zero-shot approach to hypernymy prediction using large language models (LLMs). The study employs a method based on text probability calculation, applying it to various generated prompts. The experiments demonstrate a strong correlation between the effectiveness of language model prompts and classic patterns, indicating that preliminary prompt selection can be carried out using smaller models before moving to larger ones. We also explore prompts for predicting co-hyponyms and improving hypernymy predictions by augmenting prompts with additional information through automatically identified co-hyponyms. An iterative approach is developed for predicting higher-level concepts, which further improves the quality on the BLESS dataset (MAP = 0.8).

翻译：本文研究了利用大型语言模型（LLMs）进行零样本上位词预测的方法。研究采用基于文本概率计算的方法，并将其应用于多种生成的提示（prompts）。实验表明，语言模型提示的有效性与经典模式之间存在强相关性，表明可在迁移至更大模型前，使用较小模型进行初步提示筛选。我们进一步探索了用于预测共下位词（co-hyponyms）的提示，并通过自动识别的共下位词对提示进行信息增强，从而改善上位词预测效果。此外，本文开发了一种迭代方法用于预测更高层概念，该方法在BLESS数据集上进一步提升了预测质量（MAP = 0.8）。