Papers with downstream few-shot tasks
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. |
2INER: Instructive and In-Context Learning on Few-Shot Named Entity Recognition (2023.findings-emnlp)
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| Challenge: | Named Entity Recognition (NER) tasks are a fundamental task of natural language processing (NLP). |
| Approach: | They propose a text-to-text framework for Few-Shot Named Entity Recognition (NER) that employs instruction finetuning and auxiliary tasks to enhance the model's understanding of entity types in the overall semantic context of a sentence. |
| Outcome: | The proposed framework outperforms existing Few-Shot NER methods and remains competitive with state-of-the-art NER algorithms. |