OpenPrompt: An Open-source Framework for Prompt-learning

Abstract: Prompt-learning has become a new paradigm in modern natural language processing, which directly adapts pre-trained LLMs (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}}.

• The paper introduces OpenPrompt, a unified framework that bridges pre-training and fine-tuning for consistent prompt-learning.
• It details a modular design with optimized tokenization, flexible template languages, and robust verbalizers for diverse NLP tasks.
• Empirical evaluations on benchmarks like GLUE and SuperGLUE demonstrate the framework’s efficiency, adaptability, and potential for prompt-only tuning.

OpenPrompt: An Open-source Framework for Prompt-learning

OpenPrompt addresses a significant gap in the NLP field by providing a unified and extensible toolkit for prompt-learning over pre-trained LLMs (PLMs). The paper outlines the design and implementation of OpenPrompt, emphasizing its adaptability, modularity, and efficiency in the context of prompt-learning, which is emerging as a crucial paradigm in NLP.

Motivation and Design Philosophy

Prior approaches to prompt-learning have been limited by inconsistencies and lack of standardization, often offering ad hoc solutions for specific scenarios. OpenPrompt seeks to rectify this by delivering a comprehensive framework that bridges the pre-training and fine-tuning paradigms. The toolkit supports various PLMs, such as masked LLMs (MLM), autoregressive models (LM), and sequence-to-sequence models (Seq2Seq), facilitating diverse task implementations.

Key Features and Architecture

Combinability: OpenPrompt excels in allowing researchers to flexibly combine different types of PLMs, task formats, and prompt modules, investigating the adaptability and strengths of models across a range of NLP tasks. This flexibility is key for both empirical evaluations and theoretical investigations.

Tokenization and Templates: Tokenization in OpenPrompt is specifically optimized for prompt-learning, handling nuances like token indices and concatenation issues. The innovative template language in OpenPrompt supports a range of prompt types, from hard to soft prompts, ensuring flexibility and ease of use.

Verbalizers: The framework includes robust verbalization modules for class-to-label word mapping, essential for classification tasks. It supports manual verbalizers as well as advanced automated strategies, enhancing the experimentation flexibility.

PromptModel and Training: A unified PromptModel class in OpenPrompt covers the training and inference processes, supporting both full model tuning and parameter-efficient prompt-only tuning strategies. This modularity aids in adapting easily to new methods and tasks.

Empirical Evaluation

OpenPrompt facilitates extensive evaluations across a suite of established NLP benchmarks, including GLUE, SuperGLUE, and LAMA. The architecture allows for straightforward implementation and testing of prompt-learning methods on tasks ranging from text classification to knowledge probing, demonstrating its adaptability and efficiency.

Implications and Future Directions

OpenPrompt has significant implications for both practical applications and theoretical research in NLP. By standardizing prompt-learning implementations, it simplifies the deployment of advanced NLP systems and fosters investigation into the underlying mechanisms of PLMs. Future developments aim to expand its feature set and keep pace with emerging trends and techniques in prompt-learning.

In conclusion, OpenPrompt stands as a valuable contribution to the NLP research community, providing an essential tool for exploring and applying prompt-learning methodologies with greater consistency and depth. As the field evolves, OpenPrompt is well-positioned to adapt and continue aiding researchers in uncovering new insights into the capacities and applications of PLMs.