---
license: apache-2.0
task_categories:
- question-answering
- text2text-generation
language:
- en
tags:
- chemistry
- biology
- legal
- medical
configs:
- config_name: amazon
  data_files:
  - split: test
    path: "amazon.json"
- config_name: medicine
  data_files: 
  - split: test
    path: "medicine.json"
- config_name: physics
  data_files: 
  - split: test
    path: "physics.json"
- config_name: biology
  data_files: 
  - split: test
    path: "biology.json"
- config_name: chemistry
  data_files: 
  - split: test
    path: "chemistry.json"
- config_name: computer_science
  data_files: 
  - split: test
    path: "computer_science.json"
- config_name: healthcare
  data_files: 
  - split: test
    path: "healthcare.json"
- config_name: legal
  data_files: 
  - split: test
    path: "legal.json"
- config_name: literature
  data_files: 
  - split: test
    path: "literature.json"
- config_name: material_science
  data_files: 
  - split: test
    path: "material_science.json"
---
# GRBench

<!-- Provide a quick summary of the dataset. -->

GRBench is a comprehensive benchmark dataset to support the development of methodology and facilitate the evaluation of the proposed models for Augmenting Large Language Models with External Textual Graphs.

<!--<p align="center">
  <img src="https://github.com/PeterGriffinJin/Graph-CoT/blob/main/fig/intro.png" width="400px"/>
</p>-->


## Dataset Details

### Dataset Description

<!-- Provide a longer summary of what this dataset is. -->

GRBench includes 10 real-world graphs that can serve as external knowledge sources for LLMs from five domains including academic, e-commerce, literature, healthcare, and legal domains. Each sample in GRBench consists of a manually designed question and an answer, which can be directly answered by referring to the graphs or retrieving the information from the graphs as context. To make the dataset comprehensive, we include samples of different difficulty levels: easy questions (which can be answered with single-hop reasoning on graphs), medium questions (which necessitate multi-hop reasoning on graphs), and hard questions (which call for inductive reasoning with information on graphs as context).

<!--<p align="center">
  <img src="https://github.com/PeterGriffinJin/Graph-CoT/blob/main/fig/data.png" width="300px"/>
</p>-->

- **Curated by:** Bowen Jin (https://peterjin.me/), Chulin Xie (https://alphapav.github.io/), Jiawei Zhang (https://javyduck.github.io/) and Kashob Kumar Roy (https://www.linkedin.com/in/forkkr/)
- **Language(s) (NLP):** English
- **License:** apache-2.0

### Dataset Sources

<!-- Provide the basic links for the dataset. -->

- **Repository:** https://github.com/PeterGriffinJin/Graph-CoT
- **Paper:** https://arxiv.org/pdf/2404.07103.pdf
- **Graph files:** https://drive.google.com/drive/folders/1DJIgRZ3G-TOf7h0-Xub5_sE4slBUEqy9

## Uses

<!-- Address questions around how the dataset is intended to be used. -->

### Direct Use

<!-- This section describes suitable use cases for the dataset. -->

You can first download the processed graph data here: https://drive.google.com/drive/folders/1DJIgRZ3G-TOf7h0-Xub5_sE4slBUEqy9.
Then,

```
from datasets import load_dataset
domain = 'amazon'
dataset = load_dataset("PeterJinGo/GRBench", data_files=f'{domain}.json')
```


## Dataset Structure

<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->

Information on how the graph file looks can be found here: https://github.com/PeterGriffinJin/Graph-CoT/tree/main/data.



## Dataset Creation

More details of how the dataset is constructed can be found in Section 3 of this paper (https://arxiv.org/pdf/2404.07103.pdf).

The raw graph data sources can be found here: https://github.com/PeterGriffinJin/Graph-CoT/tree/main/data/raw_data.



## Citation

<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->

**BibTeX:**

@article{jin2023graph,

  title={Graph Chain-of-Thought: Augmenting Large Language Models by Reasoning on Graphs},
  
  author={Jin, Bowen and Xie, Chulin and Zhang, Jiawei and Roy, Kashob and Zhang, Yu and Wang, Suhang and Meng, Yu and Han, Jiawei},
  
  journal={arXiv preprint arXiv:2404.07103},
  
  year={2024}
}


## Dataset Card Authors

Bowen Jin

## Dataset Card Contact

bowenj4@illinois.edu