Exploring Prompt-Based Methods for Zero-Shot Hypernym Prediction with Large Language Models
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arXiv
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866929204205977600 |
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| author | Tikhomirov, Mikhail Loukachevitch, Natalia |
| author_facet | Tikhomirov, Mikhail Loukachevitch, Natalia |
| contents | 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). |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_04515 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Exploring Prompt-Based Methods for Zero-Shot Hypernym Prediction with Large Language Models Tikhomirov, Mikhail Loukachevitch, Natalia Computation and Language Artificial Intelligence 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). |
| title | Exploring Prompt-Based Methods for Zero-Shot Hypernym Prediction with Large Language Models |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2401.04515 |