Name
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, and verbalizing strategy, etc., need to be considered in prompt-learning, practitioners face impediments to quickly adapting the desired prompt learning methods to their applications. In this paper, we present OpenPrompt, 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 formats, 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.(1) |
Year | DOI | Venue |
|---|---|---|
2022 | 10.18653/v1/2022.acl-demo.10 | PROCEEDINGS OF THE 60TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2022): PROCEEDINGS OF SYSTEM DEMONSTRATIONS |
DocType | Volume | Citations |
Conference | Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: System Demonstrations | 0 |
PageRank | References | Authors |
0.34 | 2 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ning Ding | 1 | 1 | 1.71 |
| Shengding Hu | 2 | 0 | 1.01 |
| Weilin Zhao | 3 | 0 | 0.68 |
| Yulin Chen | 4 | 0 | 0.34 |
| Zhiyuan Liu | 5 | 2037 | 123.68 |
| Zheng Hai-Tao | 6 | 32 | 9.46 |
| Maosong Sun | 7 | 2293 | 162.86 |