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
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.
Dataset Details
Dataset Description
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).
- 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
- 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
Direct Use
You can access the graph environment data for each domain here: https://drive.google.com/drive/folders/1DJIgRZ3G-TOf7h0-Xub5_sE4slBUEqy9. Then download the question answering data for each domain:
from datasets import load_dataset
domain = 'amazon' # can be selected from [amazon, medicine, physics, biology, chemistry, computer_science, healthcare, legal, literature, material_science]
dataset = load_dataset("PeterJinGo/GRBench", data_files=f'{domain}.json')
Dataset Structure
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
BibTeX:
@article{jin2024graph,
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 Kumar 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