metadata
license: cc-by-4.0
task_categories:
- text-retrieval
language:
- en
CiteGuard Dataset
CiteGuard is a benchmark and framework used in the paper CiteGuard: Faithful Citation Attribution for LLMs via Retrieval-Augmented Validation. It reframes citation evaluation as a problem of citation attribution alignment, assessing whether LLM-generated citations match those a human author would include for the same text.
- Project Page: https://kathcym.github.io/CiteGuard_Page/
- GitHub Repository: https://github.com/KathCYM/CiteGuard
Dataset Format
The dataset follows a CSV format with the following columns:
id: Unique identifier for the excerpt.excerpt: The text containing the citation to be validated (e.g., using a[CITATION]placeholder).year: The year of the source paper.source_paper_title(optional): The title of the paper containing the excerpt.target_paper_title(optional): The gold standard title(s) for evaluation.
Sample Usage
Using the CiteGuard repository, you can run the evaluation on a dataset file via the CLI:
python -m src.main --model_name gpt-4o --dataset DATASET.csv --result_path results/run.json
Citation
If you use this dataset or the CiteGuard framework, please cite the following:
@misc{choi2026citeguardfaithfulcitationattribution,
title={CiteGuard: Faithful Citation Attribution for LLMs via Retrieval-Augmented Validation},
author={Yee Man Choi and Xuehang Guo and Yi R. Fung and Qingyun Wang},
year={2026},
eprint={2510.17853},
archivePrefix={arXiv},
primaryClass={cs.DL},
url={https://arxiv.org/abs/2510.17853},
}