OpenPrompt: An Open-source Framework for Prompt-learning

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 $cloze$-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. OpenPrompt is publicly released at {\url{ https://github.com/thunlp/OpenPrompt}}.