This repository hosts the official website for the GraphRAG-Bench project, a comprehensive benchmark for evaluating Graph Retrieval-Augmented Generation models.
- Latest project updates and announcements
- Leaderboard activation status
- Introduces Graph Retrieval-Augmented Generation (GraphRAG) concept
- Compares traditional RAG vs GraphRAG approach
- Explains research objective: Identify scenarios where GraphRAG outperforms traditional RAG
- Visual comparison diagram of RAG vs GraphRAG
Two domain-specific leaderboards with comprehensive metrics:
1. GraphRAG-Bench (Novel)
- Evaluates models on literary/fictional content
2. GraphRAG-Bench (Medical)
- Evaluates models on medical/healthcare content
Evaluation Dimensions:
- Fact Retrieval (Accuracy, ROUGE-L)
- Complex Reasoning (Accuracy, ROUGE-L)
- Contextual Summarization (Accuracy, Coverage)
- Creative Generation (Accuracy, Factual Score, Coverage)
Four difficulty levels with representative examples:
Level 1: Fact Retrieval
Example: "Which region of France is Mont St. Michel located?"
Level 2: Complex Reasoning
Example: "How did Hinze's agreement with Felicia relate to the perception of England's rulers?"
Level 3: Contextual Summarization
Example: "What role does John Curgenven play as a Cornish boatman for visitors exploring this region?"
Level 4: Creative Generation
Example: "Retell King Arthur's comparison to John Curgenven as a newspaper article."
- Project email: GraphRAG@hotmail.com
The website is deployed via GitHub Pages:
https://graphrag-bench.github.io
├── css/ # Stylesheets
│ ├── fonts.css # Font definitions
│ ├── normalize.css # CSS reset
│ └── styles.css # Main styles
├── scripts.js # Interactive functionality
├── index.html # Main website file
└── RAGvsGraphRAG.jpg # Comparison diagram
git clone https://github.com/GraphRAG-Bench/GraphRAG-Bench.git
cd GraphRAG-Bench
# Open index.html in browserContributions to improve the benchmark website are welcome. Please contact the project team via email.
This repository hosts the official website for the GraphRAG-Bench project, a comprehensive benchmark for evaluating Graph Retrieval-Augmented Generation models.
- [2025-05-25] Leaderboard is on!
- Introduces Graph Retrieval-Augmented Generation (GraphRAG) concept
- Compares traditional RAG vs GraphRAG approach
- Explains research objective: Identify scenarios where GraphRAG outperforms traditional RAG
- Visual comparison diagram of RAG vs GraphRAG
Two domain-specific leaderboards with comprehensive metrics:
1. GraphRAG-Bench (Novel)
- Evaluates models on literary/fictional content
2. GraphRAG-Bench (Medical)
- Evaluates models on medical/healthcare content
Evaluation Dimensions:
- Fact Retrieval (Accuracy, ROUGE-L)
- Complex Reasoning (Accuracy, ROUGE-L)
- Contextual Summarization (Accuracy, Coverage)
- Creative Generation (Accuracy, Factual Score, Coverage)
Four difficulty levels with representative examples:
Level 1: Fact Retrieval
Example: "Which region of France is Mont St. Michel located?"
Level 2: Complex Reasoning
Example: "How did Hinze's agreement with Felicia relate to the perception of England's rulers?"
Level 3: Contextual Summarization
Example: "What role does John Curgenven play as a Cornish boatman for visitors exploring this region?"
Level 4: Creative Generation
Example: "Retell King Arthur's comparison to John Curgenven as a newspaper article."
- Project email: GraphRAG@hotmail.com
The website is deployed via GitHub Pages:
https://graphrag-bench.github.io
├── css/ # Stylesheets
│ ├── fonts.css # Font definitions
│ ├── normalize.css # CSS reset
│ └── styles.css # Main styles
├── scripts.js # Interactive functionality
├── index.html # Main website file
└── RAGvsGraphRAG.jpg # Comparison diagram
git clone https://github.com/GraphRAG-Bench/GraphRAG-Bench.git
cd GraphRAG-Benchmark
# Open index.html in browserContributions to improve the benchmark website are welcome. Please contact the project team via email.