Summary of Exploring Prompt-based Methods For Zero-shot Hypernym Prediction with Large Language Models, by Mikhail Tikhomirov and Natalia Loukachevitch
Exploring Prompt-Based Methods for Zero-Shot Hypernym Prediction with Large Language Models
by Mikhail Tikhomirov, Natalia Loukachevitch
First submitted to arxiv on: 9 Jan 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents a zero-shot method for predicting hypernymy using large language models (LLMs). The researchers use a text probability calculation-based approach to generate prompts, which are then applied to various LLMs. The results show a strong correlation between the effectiveness of these prompts and classic patterns, suggesting that smaller models can be used to select preliminary prompts before moving to larger ones. Additionally, the paper explores using prompts for predicting co-hyponyms and improving hypernymy predictions by incorporating automatically identified co-hyponyms into these prompts. The study also develops an iterative approach for predicting higher-level concepts, which leads to improved results on the BLESS dataset (MAP = 0.8). |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is all about using big language models to figure out how words are related to each other. It’s like a game of “Is A” – you know, where something is a cat and cats are animals? The researchers want to use these language models to predict when one word is more specific than another (like animal > cat). They found that if they give the model some help with what words to look at first, it gets even better at making those predictions. They also tried using this approach to predict related words and found that it made a big difference in how well it worked. |
Keywords
» Artificial intelligence » Probability » Zero shot
