Exploring Prompt-Based Methods for Zero-Shot Hypernym Prediction with Large Language Models
This work addresses hypernym prediction for NLP applications, but it is incremental as it builds on existing prompt-based techniques.
The study tackled zero-shot hypernym prediction by exploring prompt-based methods with large language models, achieving a MAP of 0.8 on the BLESS dataset through iterative approaches and prompt augmentation.
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).