Papers with prompt-based zero-shot learning
Beyond prompting: Making Pre-trained Language Models Better Zero-shot Learners by Clustering Representations (2022.emnlp-main)
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| Challenge: | Existing methods for zero-shot text classification involve heavy human engineering or complicated self-training pipelines. |
| Approach: | They propose to fit unlabeled text with a Bayesian Gaussian Mixture Model and use class names to cluster them. |
| Outcome: | The proposed approach outperforms prompt-based methods on topic and sentiment datasets and outperformed previous studies significantly on unbalanced datasets. |