Summary of Determined Multi-label Learning Via Similarity-based Prompt, by Meng Wei et al.
Determined Multi-Label Learning via Similarity-Based Prompt
by Meng Wei, Zhongnian Li, Peng Ying, Yong Zhou, Xinzheng Xu
First submitted to arxiv on: 25 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 The paper proposes a novel labeling setting called Determined Multi-Label Learning (DMLL) for multi-label classification tasks. In DMLL, each training instance is associated with either “Yes” or “No”, indicating whether it contains the provided class label. This reduces annotation costs and allows for efficient learning of multi-label classifiers using risk-consistent estimators. The approach also incorporates a similarity-based prompt learning method to minimize loss and learn supplemental prompts. Experimental results demonstrate superior performance compared to existing state-of-the-art methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper helps with a big problem in machine learning called multi-label classification. Right now, it takes a lot of time and effort to label all the data correctly. This makes it hard for people to use these techniques in real-life situations. To make things easier, the authors came up with a new way of labeling called Determined Multi-Label Learning (DMLL). In DMLL, each piece of training data is given a simple “Yes” or “No” label that tells you if it belongs to a certain category. This makes it much faster and cheaper to prepare the data for learning. The authors also came up with a new way to help big language models learn more about what they’re doing. They did lots of tests to see how well this works, and it turns out it’s actually really good! |
Keywords
* Artificial intelligence * Classification * Machine learning * Prompt
