PromptKG is a comprehensive gallery and toolkit for researchers and practitioners in prompt learning and knowledge graph (KG) integration with large language models (LLMs). It provides implementations of state-of-the-art prompt learning research, libraries for KG embeddings and dynamic editing, and extensive tutorial notebooks for beginners. The project aims to consolidate and advance the rapidly evolving field of using prompts to leverage and enhance KG capabilities within LLMs.

How It Works

The project organizes its content into several key areas: research for model implementations, lambdaKG for PLM-based KG embeddings, and deltaKG for dynamic KG embedding editing. It also curates a vast list of papers and tutorials covering various aspects of prompt learning, knowledge probing, KG construction, and multimodal applications, offering a structured overview of the field.

Quick Start & Requirements

• Installation and usage details are primarily found within the respective sub-directories (research, lambdaKG, deltaKG).
• Specific requirements will vary based on the chosen toolkit or research implementation, likely including Python and deep learning frameworks (e.g., PyTorch, TensorFlow).
• Links to tutorials and papers are provided within the README for guided learning.

Highlighted Details

• Extensive collection of research papers and implementations on prompt learning for NLP and KG tasks.
• Dedicated libraries (lambdaKG, deltaKG) for practical KG embedding and manipulation with PLMs.
• Comprehensive tutorials covering zero-shot learning, KG construction, and prompt-based knowledge integration.
• Covers a wide range of applications including language understanding, multimodal tasks, and advanced areas like recommendation systems and robotics.

Maintenance & Community

• The project is actively maintained, as indicated by the last commit badge.
• Pull requests are welcomed, suggesting an open community engagement model.
• Contact is via GitHub issues for support.

Licensing & Compatibility

• The project is licensed under the MIT License, permitting commercial use and modification.

Limitations & Caveats

• As a gallery and collection, the project itself does not provide a single unified API; users must interact with individual research implementations or libraries.
• The breadth of the content means specific setup and compatibility requirements will vary significantly across different components.