Hugging Face's logo

Datasets:
UCSC-IRKM
/
RAGuard

Tasks:
Question Answering
Languages:
English
Size:
10K<n<100K
ArXiv:
License:
Dataset card Data Studio Files Community
RAGuard / README.md
RithwikG's picture
RithwikG
Update README.md
3e036eb verified
preview code
|
raw
history blame contribute delete
2.54 kB
metadata
license: mit
task_categories:
  - question-answering
language:
  - en
pretty_name: RAGuard
size_categories:
  - 10K<n<100K

Dataset Card for RAGuard

Dataset Details

RAGuard is a fact-checking dataset designed to evaluate the robustness of RAG systems against misleading retrievals. It consists of 2,648 political claims made by U.S. presidential candidates (2000–2024), each labeled as either true or false, and a knowledge base comprising 16,331 documents. Each claim is linked to a set of associated documents, categorized as supporting, misleading, or irrelevant

Dataset Description

claims.csv contains the following fields for each claim, scraped from PolitiFact.

  1. Claim ID
  2. Claim: Full text of the claim
  3. Verdict: Binary fact-checking verdict, True or False
  4. Document IDs: List IDs of documents corresponding to this claim
  5. Document Labels: List of labels for the associated documents, either supporting, misleading, or irrelevant

documents.csv contains the following fields for each document in our knowledge base, scraped from Reddit.

  1. Document ID
  2. Title: Reddit post title
  3. Full Text: Content of the document
  4. Claim ID: ID of the corresponding claim
  5. Document Label: Label for the document's label to the claim, either supporting, misleading, or irrelevant
  6. Link: URL to the original document

Dataset Source and Usage

  • See Section 2.4 of our paper for supported tasks

Citation

If you use this dataset, please cite our paper:

@misc{zeng2025worsezeroshotfactcheckingdataset,
      title={Worse than Zero-shot? A Fact-Checking Dataset for Evaluating the Robustness of RAG Against Misleading Retrievals}, 
      author={Linda Zeng and Rithwik Gupta and Divij Motwani and Diji Yang and Yi Zhang},
      year={2025},
      eprint={2502.16101},
      archivePrefix={arXiv},
      primaryClass={cs.AI},
      url={https://arxiv.org/abs/2502.16101}, 
}

Disclaimer

This dataset has been compiled from publicly available sources on the Internet. It may contain discussions on sensitive political topics, including viewpoints that some individuals may find controversial or offensive. The inclusion of any content does not imply endorsement of any views expressed. Users are advised to exercise discretion and ensure compliance with applicable ethical guidelines and legal frameworks when using this dataset.