Introduction to PromptPapers
PromptPapers is a curated list of research papers focusing on the emerging field of prompt-based tuning for large-scale pre-trained language models. It diverges from the traditional fine-tuning approach by utilizing pre-trained models to execute pre-training tasks like classification or regression directly. This paper list serves as a comprehensive resource for understanding the latest advancements and trends in the field of prompt-learning.
Prompt-learning, an innovative technique in the machine learning arena, aims to adapt pre-trained models more effectively by structuring the training procedures and unifying different tasks. This is akin to delta tuning, which enhances pre-trained models by concentrating on specific optimization strategies. For those interested, there is also a similar repository titled DeltaPapers, which explores delta tuning further.
Open-Source Toolkit
A key highlight of the PromptPapers project is the release of OpenPrompt, an open-source toolkit designed for prompt-learning. This toolkit allows researchers and developers to experiment and innovate with prompt-based approaches in their projects. The project encourages the academic community to contribute actively by updating the paper's repository through pull requests, thus nurturing a collaborative environment for advancements in prompt-learning techniques.
Keywords Convention
PromptPapers adopts a standardized convention using colored badges to signify certain attributes of the included works. These include:
- T5 (blue): Denotes the abbreviation of the work.
- Continuous_Template (red): Highlights key prompt learning features.
- Generation (brown): Indicates the main focus task of the study.
- Analysis (green): Shows the primary prompt-learning properties analyzed.
Overview of Key Sections
Papers Categorization
PromptPapers organizes its collection into multiple categories to provide a structured approach to understanding the field:
-
Overview: Papers outlining the broad trends in utilizing pre-trained language models in natural language processing (NLP).
-
Pilot Work: Early research contributions that have significantly influenced the adoption and development of prompt-learning paradigms.
-
Basics: Exploration of fundamental aspects of prompt-tuning, including template design, verbalizers, and training paradigms.
-
Analysis: Papers providing insights and critical examinations of prompt-learning methods.
Contribution
The PromptPapers project is maintained by Ning Ding and Shengding Hu, who, along with other contributors, encourage community involvement. They offer guidelines for contributing to this evolving resource, ensuring it remains up-to-date with cutting-edge research developments.
Conclusion
PromptPapers is an invaluable resource for anyone interested in prompt-based learning and the adaptation of pre-trained language models. By compiling an organized list of essential papers, supported by an open-source toolkit (OpenPrompt), the project fosters a robust and collaborative research community that continually pushes the boundaries of what's possible in NLP. Whether you're a researcher, a developer, or just curious about NLP, PromptPapers offers the insights and tools needed to stay informed and active in this dynamic field.