Summary of Manual Verbalizer Enrichment For Few-shot Text Classification, by Quang Anh Nguyen et al.
Manual Verbalizer Enrichment for Few-Shot Text Classification
by Quang Anh Nguyen, Nadi Tomeh, Mustapha Lebbah, Thierry Charnois, Hanene Azzag, Santiago Cordoba Muñoz
First submitted to arxiv on: 8 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 proposed approach, MAVE, enables the construction of verbalizers by enriching class labels using neighborhood relations in the word embedding space for text classification tasks. By leveraging this method, the model achieves state-of-the-art results while utilizing significantly fewer resources. The framework is particularly effective in scenarios with extremely limited supervision data. Compared to traditional fine-tuning, prompt-based training shows great performance in zero-shot or few-shot scenarios where annotated data is limited. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers has developed a new way to help computers understand and generate text. They call it MAVE (Masked Argument Visualization Engine). This system can take a set of words and turn them into a meaningful sentence. The best part? It only needs a small amount of training data, which is helpful when you’re dealing with limited information. In fact, the team’s method works better than usual in situations where there’s very little data to work with. |
Keywords
» Artificial intelligence » Embedding space » Few shot » Fine tuning » Prompt » Text classification » Zero shot
