@inproceedings{ding-etal-2022-openprompt,
title = "{O}pen{P}rompt: An Open-source Framework for Prompt-learning",
author = "Ding, Ning and
Hu, Shengding and
Zhao, Weilin and
Chen, Yulin and
Liu, Zhiyuan and
Zheng, Haitao and
Sun, Maosong",
editor = "Basile, Valerio and
Kozareva, Zornitsa and
Stajner, Sanja",
booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: System Demonstrations",
month = may,
year = "2022",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.acl-demo.10",
doi = "10.18653/v1/2022.acl-demo.10",
pages = "105--113",
abstract = "Prompt-learning has become a new paradigm in modern natural language processing, which directly adapts pre-trained language models (PLMs) to cloze-style prediction, autoregressive modeling, or sequence to sequence generation, resulting in promising performances on various tasks. However, no standard implementation framework of prompt-learning is proposed yet, and most existing prompt- learning codebases, often unregulated, only provide limited implementations for specific scenarios. Since there are many details such as templating strategy, initializing strategy, verbalizing strategy, etc., that need to be considered in prompt-learning, practitioners face impediments to quickly adapting the de-sired prompt learning methods to their applications. In this paper, we present Open- Prompt, a unified easy-to-use toolkit to conduct prompt-learning over PLMs. OpenPrompt is a research-friendly framework that is equipped with efficiency, modularity, and extendibility, and its combinability allows the freedom to combine different PLMs, task for- mats, and prompting modules in a unified paradigm. Users could expediently deploy prompt-learning frameworks and evaluate the generalization of them on different NLP tasks without constraints.",
}
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<abstract>Prompt-learning has become a new paradigm in modern natural language processing, which directly adapts pre-trained language models (PLMs) to cloze-style prediction, autoregressive modeling, or sequence to sequence generation, resulting in promising performances on various tasks. However, no standard implementation framework of prompt-learning is proposed yet, and most existing prompt- learning codebases, often unregulated, only provide limited implementations for specific scenarios. Since there are many details such as templating strategy, initializing strategy, verbalizing strategy, etc., that need to be considered in prompt-learning, practitioners face impediments to quickly adapting the de-sired prompt learning methods to their applications. In this paper, we present Open- Prompt, a unified easy-to-use toolkit to conduct prompt-learning over PLMs. OpenPrompt is a research-friendly framework that is equipped with efficiency, modularity, and extendibility, and its combinability allows the freedom to combine different PLMs, task for- mats, and prompting modules in a unified paradigm. Users could expediently deploy prompt-learning frameworks and evaluate the generalization of them on different NLP tasks without constraints.</abstract>
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%0 Conference Proceedings
%T OpenPrompt: An Open-source Framework for Prompt-learning
%A Ding, Ning
%A Hu, Shengding
%A Zhao, Weilin
%A Chen, Yulin
%A Liu, Zhiyuan
%A Zheng, Haitao
%A Sun, Maosong
%Y Basile, Valerio
%Y Kozareva, Zornitsa
%Y Stajner, Sanja
%S Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: System Demonstrations
%D 2022
%8 May
%I Association for Computational Linguistics
%C Dublin, Ireland
%F ding-etal-2022-openprompt
%X Prompt-learning has become a new paradigm in modern natural language processing, which directly adapts pre-trained language models (PLMs) to cloze-style prediction, autoregressive modeling, or sequence to sequence generation, resulting in promising performances on various tasks. However, no standard implementation framework of prompt-learning is proposed yet, and most existing prompt- learning codebases, often unregulated, only provide limited implementations for specific scenarios. Since there are many details such as templating strategy, initializing strategy, verbalizing strategy, etc., that need to be considered in prompt-learning, practitioners face impediments to quickly adapting the de-sired prompt learning methods to their applications. In this paper, we present Open- Prompt, a unified easy-to-use toolkit to conduct prompt-learning over PLMs. OpenPrompt is a research-friendly framework that is equipped with efficiency, modularity, and extendibility, and its combinability allows the freedom to combine different PLMs, task for- mats, and prompting modules in a unified paradigm. Users could expediently deploy prompt-learning frameworks and evaluate the generalization of them on different NLP tasks without constraints.
%R 10.18653/v1/2022.acl-demo.10
%U https://aclanthology.org/2022.acl-demo.10
%U https://doi.org/10.18653/v1/2022.acl-demo.10
%P 105-113
Markdown (Informal)
[OpenPrompt: An Open-source Framework for Prompt-learning](https://aclanthology.org/2022.acl-demo.10) (Ding et al., ACL 2022)
ACL
- Ning Ding, Shengding Hu, Weilin Zhao, Yulin Chen, Zhiyuan Liu, Haitao Zheng, and Maosong Sun. 2022. OpenPrompt: An Open-source Framework for Prompt-learning. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pages 105–113, Dublin, Ireland. Association for Computational Linguistics.