@inproceedings{liang-etal-2022-contrastive,
title = "Contrastive Demonstration Tuning for Pre-trained Language Models",
author = "Liang, Xiaozhuan and
Zhang, Ningyu and
Cheng, Siyuan and
Zhang, Zhenru and
Tan, Chuanqi and
Chen, Huajun",
editor = "Goldberg, Yoav and
Kozareva, Zornitsa and
Zhang, Yue",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2022",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.findings-emnlp.56",
doi = "10.18653/v1/2022.findings-emnlp.56",
pages = "799--811",
abstract = "Pretrained language models can be effectively stimulated by textual prompts or demonstrations, especially in low-data scenarios. Recent works have focused on automatically searching discrete or continuous prompts or optimized verbalizers, yet studies for the demonstration are still limited. Concretely, the demonstration examples are crucial for an excellent final performance of prompt-tuning. In this paper, we propose a novel pluggable, extensible, and efficient approach named contrastive demonstration tuning, which is free of demonstration sampling. Furthermore, the proposed approach can be: (i) Plugged into any previous prompt-tuning approaches; (ii) Extended to widespread classification tasks with a large number of categories. Experimental results on 16 datasets illustrate that our method integrated with previous approaches LM-BFF and P-tuning can yield better performance. Code is available in \url{https://github.com/zjunlp/PromptKG/tree/main/research/Demo-Tuning}.",
}
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<abstract>Pretrained language models can be effectively stimulated by textual prompts or demonstrations, especially in low-data scenarios. Recent works have focused on automatically searching discrete or continuous prompts or optimized verbalizers, yet studies for the demonstration are still limited. Concretely, the demonstration examples are crucial for an excellent final performance of prompt-tuning. In this paper, we propose a novel pluggable, extensible, and efficient approach named contrastive demonstration tuning, which is free of demonstration sampling. Furthermore, the proposed approach can be: (i) Plugged into any previous prompt-tuning approaches; (ii) Extended to widespread classification tasks with a large number of categories. Experimental results on 16 datasets illustrate that our method integrated with previous approaches LM-BFF and P-tuning can yield better performance. Code is available in https://github.com/zjunlp/PromptKG/tree/main/research/Demo-Tuning.</abstract>
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%0 Conference Proceedings
%T Contrastive Demonstration Tuning for Pre-trained Language Models
%A Liang, Xiaozhuan
%A Zhang, Ningyu
%A Cheng, Siyuan
%A Zhang, Zhenru
%A Tan, Chuanqi
%A Chen, Huajun
%Y Goldberg, Yoav
%Y Kozareva, Zornitsa
%Y Zhang, Yue
%S Findings of the Association for Computational Linguistics: EMNLP 2022
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates
%F liang-etal-2022-contrastive
%X Pretrained language models can be effectively stimulated by textual prompts or demonstrations, especially in low-data scenarios. Recent works have focused on automatically searching discrete or continuous prompts or optimized verbalizers, yet studies for the demonstration are still limited. Concretely, the demonstration examples are crucial for an excellent final performance of prompt-tuning. In this paper, we propose a novel pluggable, extensible, and efficient approach named contrastive demonstration tuning, which is free of demonstration sampling. Furthermore, the proposed approach can be: (i) Plugged into any previous prompt-tuning approaches; (ii) Extended to widespread classification tasks with a large number of categories. Experimental results on 16 datasets illustrate that our method integrated with previous approaches LM-BFF and P-tuning can yield better performance. Code is available in https://github.com/zjunlp/PromptKG/tree/main/research/Demo-Tuning.
%R 10.18653/v1/2022.findings-emnlp.56
%U https://aclanthology.org/2022.findings-emnlp.56
%U https://doi.org/10.18653/v1/2022.findings-emnlp.56
%P 799-811
Markdown (Informal)
[Contrastive Demonstration Tuning for Pre-trained Language Models](https://aclanthology.org/2022.findings-emnlp.56) (Liang et al., Findings 2022)
ACL
- Xiaozhuan Liang, Ningyu Zhang, Siyuan Cheng, Zhenru Zhang, Chuanqi Tan, and Huajun Chen. 2022. Contrastive Demonstration Tuning for Pre-trained Language Models. In Findings of the Association for Computational Linguistics: EMNLP 2022, pages 799–811, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.