CiteME is a benchmark designed to test the abilities of language models in finding papers that are cited in scientific texts.

Dataset Structure

The dataset is provided in CSV format and includes the following columns:

Column Name	Description
id	A unique identifier for each paper, used consistently across all experiments.
excerpt	The text excerpt from the source paper that describes the target paper.
target_paper_title	The title of the paper that is being cited in the excerpt.
target_paper_url	The URL linking to the target paper.
source_paper_title	The title of the paper from which the excerpt is taken.
source_paper_url	The URL linking to the source paper.
year	The publication year of the source paper.
split	Indicates the dataset split: train or test.

Example

id	excerpt	target_paper_title	target_paper_url	source_paper_title	source_paper_url	year	split
1	"As demonstrated in [Smith et al., 2020], the proposed method improves accuracy significantly."	"Improving Accuracy in ML Models"	https://example.com/target1	"Advancements in Machine Learning"	https://example.com/source1	2020	train
2	"Building upon the framework introduced by [Doe, 2019], we extend the applicability to NLP tasks."	"Framework for NLP Applications"	https://example.com/target2	"Foundations of NLP"	https://example.com/source2	2019	test

Load the Dataset

You can load the dataset using popular data processing libraries such as pandas.

import pandas as pd

dataset = pd.read_csv('DATASET.csv')
print(dataset.head())

Dataset Structure

If you find our work helpful, please use the following citation:

@misc{press2024citeme,
    title={CiteME: Can Language Models Accurately Cite Scientific Claims?},
    author={Ori Press and Andreas Hochlehnert and Ameya Prabhu and Vishaal Udandarao and Ofir Press and Matthias Bethge},
    year={2024},
    eprint={2407.12861},
    archivePrefix={arXiv},
    primaryClass={cs.AI},
    url={https://arxiv.org/abs/2407.12861}
}