license: apache-2.0
task_categories:
- visual-question-answering
- zero-shot-classification
language:
- en
tags:
- fact-checking
- claim-verification
- multimodal
pretty_name: ClaimReview2024+
size_categories:
- n<1K
extra_gated_prompt: >-
**Terms of Use**: The dataset contains images that, by law, are protected by
copyright. Therefore, the dataset **must not** be published to the broad
public. Only researchers, educators, and students in the field of automated
fact-checking may get access to this dataset—for **non-commercial** use only.
extra_gated_fields:
First name: text
Last name: text
Institutional email: text
Affiliation: text
Country: country
I want to use this dataset for:
type: select
options:
- Research
- Education
I agree to use this dataset for non-commercial use ONLY: checkbox
ClaimReview2024+ Benchmark
This is the ClaimReview2024+ (CR+) benchmark, a dataset used to evaluate multimodal automated fact-checking systems. The task is to classify each claim as either supported, refuted, misleading, or not enough information. CR+ consists of 300 real-world claims sourced via the ClaimReview markup from professional fact-checking articles. CR+ was specifically constructed to avoid the data leakage problem in which claims released prior to GPT-4o's knowledge cutoff in October 2023 are known to GPT-4o. Hence, CR+ only contains claims from fact-checking articles released starting Nov 1, 2023. Out of the 300 instances, 140 contain an image, the others are text only.
CR+ was constructed along with DEFAME, the current state-of-the-art multimodal fact-checking system and the first that can handle both multimodal claims and multimodal evidence. DEFAME achieved an accuracy of 69.7% on CR+.
For more details on CR+, check out the ICML paper.
Examples
Cite this Work
Please use the following BibTeX to refer to the authors:
@inproceedings{braun2024defame,
title = {{DEFAME: Dynamic Evidence-based FAct-checking with Multimodal Experts}},
author = {Tobias Braun and Mark Rothermel and Marcus Rohrbach and Anna Rohrbach},
booktitle = {Proceedings of the 42nd International Conference on Machine Learning},
year = {2025},
url = {https://arxiv.org/abs/2412.10510},
}