  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).
