Papers with Prompt-based tuning
PromptDA: Label-guided Data Augmentation for Prompt-based Few Shot Learners (2023.eacl-main)
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| Challenge: | Existing studies on prompt-based few-shot tuning focus on deriving proper label words with a verbalizer or generating prompt templates to elicit semantics from PLMs. |
| Approach: | They propose a framework that leverages label semantics for prompt-based tuning. |
| Outcome: | The proposed framework improves on few-shot text classification tasks by leveraging label semantics and data augmentation. |
LM-CPPF: Paraphrasing-Guided Data Augmentation for Contrastive Prompt-Based Few-Shot Fine-Tuning (2023.acl-short)
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| Challenge: | Recent advances in pre-trained language models have been limited when fine-tuned on small datasets. |
| Approach: | They propose to add contrastive learning to prompt-based fine-tuning to improve model performance. |
| Outcome: | The proposed approach outperforms other methods on multiple text classification benchmarks. |
Prototypical Verbalizer for Prompt-based Few-shot Tuning (2022.acl-long)
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| Challenge: | Prompt-based tuning for pre-trained language models has shown its effectiveness in few-shot learning. |
| Approach: | They propose a prototypical verbalizer which learns prototype vectors as verbalizes by contrastive learning. |
| Outcome: | The proposed verbalizer outperforms existing verbalizing methods on topic classification and entity typing tasks. |
Zero-shot Cross-lingual Transfer of Prompt-based Tuning with a Unified Multilingual Prompt (2022.emnlp-main)
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| Challenge: | Existing work focuses on monolingual prompts, but we study multilingual prompt for multilingual models. |
| Approach: | They propose a model that uses a unified prompt for all languages, called UniPrompt, to alleviate the effort of designing different prompts for multiple languages. |
| Outcome: | The proposed model outperforms baseline models in the zero-shot cross-lingual setting. |