Poly-FEVER / README.md
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---
language:
- en
- zh
- hi
- ar
- bn
- ja
- ko
- ta
- th
- ka
- am
size_categories:
- 10K<n<100K
task_categories:
- text-classification
---
# Dataset Card for Poly-FEVER
<!-- Provide a quick summary of the dataset. -->
This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1).
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
Poly-FEVER is a multilingual fact verification benchmark designed to evaluate hallucination detection in large language models (LLMs). It extends three widely used fact-checking datasets—FEVER, Climate-FEVER, and SciFact—by translating claims into 11 languages, enabling cross-linguistic analysis of LLM performance.
Poly-FEVER consists of 77,973 factual claims with binary labels (SUPPORTS or REFUTES), making it suitable for benchmarking multilingual hallucination detection. The dataset covers various domains, including Arts, Science, Politics, and History.
- **Funded by [optional]:** Google Cloud Translation
- **Language(s) (NLP):** English(en), Mandarin Chinese (zh-CN), Hindi (hi), Arabic (ar), Bengali (bn), Japanese (ja), Korean (ko), Tamil (ta), Thai (th), Georgian (ka), and Amharic (am)
### Dataset Sources [optional]
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- **Repository:** [More Information Needed]
- **Paper [optional]:** [https://huggingface.co/papers/2503.16541]
- **Demo [optional]:** [More Information Needed]
## Uses
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### Direct Use
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[More Information Needed]
### Out-of-Scope Use
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[More Information Needed]
## Dataset Structure
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[More Information Needed]
## Dataset Creation
### Curation Rationale
<!-- Motivation for the creation of this dataset. -->
[More Information Needed]
### Source Data
FEVER: https://fever.ai/resources.html
CLIMATE-FEVER: https://www.sustainablefinance.uzh.ch/en/research/climate-fever.html
SciFact: https://huggingface.co/datasets/allenai/scifact
#### Data Collection and Processing
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[More Information Needed]
### Annotations [optional]
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#### Annotation process
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[More Information Needed]
## Bias, Risks, and Limitations
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[More Information Needed]
### Recommendations
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Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
## Citation [optional]
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**BibTeX:**
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**APA:**
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## Glossary [optional]
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## More Information [optional]
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## Dataset Card Authors [optional]
[More Information Needed]
## Dataset Card Contact
Hanzhi Zhang