Summary of Chinese Metaphor Recognition Using a Multi-stage Prompting Large Language Model, by Jie Wang et al.
Chinese Metaphor Recognition Using a Multi-stage Prompting Large Language Model
by Jie Wang, Jin Wang, Xuejie Zhang
First submitted to arxiv on: 17 Aug 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 study proposes a multi-stage generative heuristic-enhanced prompt framework to improve Large Language Models’ (LLMs) ability to recognize tenors, vehicles, and grounds in Chinese metaphors. The framework consists of three stages: first, a small model is trained to generate answer candidates; second, questions are clustered and sampled according to specific rules; and finally, the heuristic-enhanced prompt needed is formed by combining generated answer candidates and demonstrations. The proposed model achieved top results in several tracks at NLPCC-2024 Shared Task 9, demonstrating its effectiveness in enhancing LLMs’ metaphor understanding capabilities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research aims to help computers better understand metaphors in Chinese texts. Currently, most models can only recognize metaphors when the words “tenor” and “vehicle” are included. However, this doesn’t work for all situations. The researchers propose a new method that trains a computer model to identify these parts of metaphors even when they’re not there. This approach achieved excellent results in a competition, showing its potential to improve computers’ understanding of metaphors. |
Keywords
» Artificial intelligence » Prompt
