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96 | Which thing has over 4 joint views with Cleto Reyes Plastic Boxing Glove Keychain - Red? | Ringside Small Boxing Glove Keyring, Ringside Boxing Glove Key Ring |
97 | Which item shares over 4 co-viewed items with item Belkin Wireless Play Max Router (F7D4301)? | Belkin Share N300 Wireless N+ Router MiMo 3D & USB Port, ASUS RT-N66U Dual-Band Wireless-N900 Gigabit Router, TP-LINK TL-WR841N Wireless N300 Home Router, 300Mpbs, IP QoS, WPS Button, Belkin Play N600 HD Wireless Dual Band N+ Router, Belkin F7D8302 Play N600 Wireless Dual-Band N Router, up to 300Mbps, Belkin N300 Wireless N Router (Latest Generation), Belkin N300 Wireless N Router (Older Generation), Belkin N450 Wireless N Router (Latest Generation), Belkin N750 Wireless DB Dual-Band Gigabit N+ Router With High Speed HD Streaming & USB (F9K1103), Linksys E2500 (N600) Advanced Simultaneous Dual-Band Wireless-N Router, Belkin Wireless Play Router (F7D4302), Belkin N600 Wireless Dual-Band N+ Router (Latest Generation), Belkin Surf N300 Wireless N Router (Older Generation), Belkin Share Max N300 Wireless N+ MiMo Router 3D + 2USB + Gigabit, Belkin Play N600 Wireless Dual-Band N+ Router - F7D9302/F7D8302, Belkin N150 Wireless/WiFi N Router (Latest Generation) - Packaging may vary, NETGEAR RangeMax WNR1000 Wireless Router |
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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
Dataset Card Contact
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