OpenPrompt: An Open-source Framework for Prompt-learning
Ning Ding, Shengding Hu, Weilin Zhao, Yulin Chen, Zhiyuan Liu, Hai-Tao, Zheng, Maosong Sun

TL;DR
OpenPrompt is an open-source, modular framework designed to simplify and standardize prompt-learning with pre-trained language models, enabling flexible application across various NLP tasks.
Contribution
It introduces a unified, extendable toolkit for prompt-learning, addressing the lack of standard implementations and facilitating rapid deployment and evaluation.
Findings
Provides a modular, extendable framework for prompt-learning
Enables quick deployment and evaluation across NLP tasks
Supports combining different PLMs, task formats, and prompting modules
Abstract
Prompt-learning has become a new paradigm in modern natural language processing, which directly adapts pre-trained language models (PLMs) to -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…
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Taxonomy
TopicsTopic Modeling · Natural Language Processing Techniques · Text Readability and Simplification
