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        <title>PromptDA: Label-guided Data Augmentation for Prompt-based Few Shot Learners</title>
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            <namePart type="family">Vlachos</namePart>
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    <abstract>Recent advances in large pre-trained language models (PLMs) lead to impressive gains on natural language understanding (NLU) tasks with task-specific fine-tuning. However, directly fine-tuning PLMs heavily relies on sufficient labeled training instances, which are usually hard to obtain. Prompt-based tuning on PLMs has shown to be powerful for various downstream few-shot tasks. Existing works studying prompt-based tuning for few-shot NLU tasks mainly focus on deriving proper label words with a verbalizer or generating prompt templates to elicit semantics from PLMs. In addition, conventional data augmentation strategies such as synonym substitution are also widely adopted in low-resource scenarios. However, the improvements they bring to prompt-based few-shot learning have been demonstrated to be marginal. Thus, an important research question arises as follows: how to design effective data augmentation methods for prompt-based few-shot tuning? To this end, considering the label semantics are essential in prompt-based tuning, we propose a novel label-guided data augmentation framework PromptDA, which exploits the enriched label semantic information for data augmentation. Extensive experiment results on few-shot text classification tasks show that our proposed framework achieves superior performances by effectively leveraging label semantics and data augmentation for natural language understanding.</abstract>
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%0 Conference Proceedings
%T PromptDA: Label-guided Data Augmentation for Prompt-based Few Shot Learners
%A Chen, Canyu
%A Shu, Kai
%Y Vlachos, Andreas
%Y Augenstein, Isabelle
%S Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics
%D 2023
%8 May
%I Association for Computational Linguistics
%C Dubrovnik, Croatia
%F chen-shu-2023-promptda
%X Recent advances in large pre-trained language models (PLMs) lead to impressive gains on natural language understanding (NLU) tasks with task-specific fine-tuning. However, directly fine-tuning PLMs heavily relies on sufficient labeled training instances, which are usually hard to obtain. Prompt-based tuning on PLMs has shown to be powerful for various downstream few-shot tasks. Existing works studying prompt-based tuning for few-shot NLU tasks mainly focus on deriving proper label words with a verbalizer or generating prompt templates to elicit semantics from PLMs. In addition, conventional data augmentation strategies such as synonym substitution are also widely adopted in low-resource scenarios. However, the improvements they bring to prompt-based few-shot learning have been demonstrated to be marginal. Thus, an important research question arises as follows: how to design effective data augmentation methods for prompt-based few-shot tuning? To this end, considering the label semantics are essential in prompt-based tuning, we propose a novel label-guided data augmentation framework PromptDA, which exploits the enriched label semantic information for data augmentation. Extensive experiment results on few-shot text classification tasks show that our proposed framework achieves superior performances by effectively leveraging label semantics and data augmentation for natural language understanding.
%R 10.18653/v1/2023.eacl-main.41
%U https://aclanthology.org/2023.eacl-main.41
%U https://doi.org/10.18653/v1/2023.eacl-main.41
%P 562-574

Markdown (Informal)

[PromptDA: Label-guided Data Augmentation for Prompt-based Few Shot Learners](https://aclanthology.org/2023.eacl-main.41) (Chen & Shu, EACL 2023)

• PromptDA: Label-guided Data Augmentation for Prompt-based Few Shot Learners (Chen & Shu, EACL 2023)

ACL

• Canyu Chen and Kai Shu. 2023. PromptDA: Label-guided Data Augmentation for Prompt-based Few Shot Learners. In Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics, pages 562–574, Dubrovnik, Croatia. Association for Computational Linguistics.