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).

该研究通过使用大型语言模型（LLMs）对零样本超级类别预测进行研究，基于文本概率计算方法，进行多种生成提示的应用。实验证明，语言模型提示的有效性与经典模式之间存在着强关联，这表明可以在使用较大模型之前，通过较小模型进行初步提示选择。我们还通过自动识别的共同下义词，探究了预测共同下义词和提高超级类别预测的提示增强方法。针对预测更高级概念，我们开发了一个迭代式方法，这进一步提高了 BLESS 数据集的质量（MAP = 0.8）。

利用大型语言模型探索零样本上位词预测的基于提示的方法

Exploring Prompt-Based Methods for Zero-Shot Hypernym Prediction with  Large Language Models

This paper presents a comparison of unsupervised methods of hypernymy
prediction (i.e., to predict which word in a pair of words such as fish-cod is
the hypernym and which the hyponym). Most importantly, we demonstrate across
datasets for English and for German that the predictions of three methods
(WeedsPrec, invCL, SLQS Row) strongly overlap and are highly correlated with
frequency-based predictions. In contrast, the second-order method SLQS shows an
overall lower accuracy but makes correct predictions where the others go wrong.
Our study once more confirms the general need to check the frequency bias of a
computational method in order to identify frequency-(un)related effects.

该论文比较了无监督的上位词预测方法，特别是在英语和德语的数据集上证明了三种方法的预测结果与基于频率的预测结果高度重叠和高度相关；然而，第二种方法 SLQS 虽然整体准确度较低，但在其他方法出错时可以进行正确的预测。该研究再次证实了需要检查计算方法中的频率偏见以识别与频率相关的效应的普遍需求。