Summary of A Novel Prompt-tuning Method: Incorporating Scenario-specific Concepts Into a Verbalizer, by Yong Ma et al.
A Novel Prompt-tuning Method: Incorporating Scenario-specific Concepts into a Verbalizer
by Yong Ma, Senlin Luo, Yu-Ming Shang, Zhengjun Li, Yong Liu
First submitted to arxiv on: 10 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 novel approach to constructing verbalizers, which are essential for prompt-tuning in machine learning models. Existing methods rely on augmenting sets of synonyms or related words based on class names, but this paradigm has limitations. The proposed method incorporates scenario-specific concepts and uses a cascade calibration module to refine label-word candidates into a set of label words for each class. The approach is evaluated on five widely used datasets for zero-shot text classification, outperforming existing methods and achieving state-of-the-art results. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers have developed a new way to create verbalizers that can help machine learning models learn from text data. They took a different approach than previous methods by using concepts from specific scenarios to generate label words. This new method is tested on five different datasets for classifying text and shows better results than other approaches. |
Keywords
» Artificial intelligence » Machine learning » Prompt » Text classification » Zero shot
