Contrastive Demonstration Tuning for Pre-trained Language Models (2022.findings-emnlp)
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| Challenge: | Recent studies focus on searching discrete or continuous prompts or optimized verbalizers, yet the demonstration examples are crucial for an excellent final performance of prompt-tuning. |
| Approach: | They propose a pluggable, extensible, and efficient approach to prompt tuning that is free of demonstration sampling. |
| Outcome: | The proposed approach can be pluggable, extensible, and efficient on 16 datasets. |
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| Challenge: | Prompt-based tuning for pre-trained language models has shown its effectiveness in few-shot learning. |
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| Challenge: | a contrastive learning framework is used to fine-tune pre-trained language models with unlabeled sentences or labeled sentences. |
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| Challenge: | Large language models for machine translation often face difficulties in leveraging demonstrations to further improve their performance. |
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