Papers with Perplexity Selection
What Makes Pre-trained Language Models Better Zero-shot Learners? (2023.acl-long)
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| Challenge: | Current methods for prompt learning in zero-shot scenarios rely on a development set with sufficient human-annotated data to select the best-performing prompt template. |
| Approach: | They propose a method for screening reasonable prompt templates in zero-shot text classification using language discrepancy. |
| Outcome: | The proposed method improves prediction performance in a realistic zero-shot setting, eliminating the need for labelled examples. |