metadata

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

This dataset card aims to be a base template for new datasets. It has been generated using this raw template.

Dataset Details

Dataset Description

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]

Repository: [More Information Needed]
Paper [optional]: [https://huggingface.co/papers/2503.16541]
Demo [optional]: [More Information Needed]

Uses

Direct Use

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Dataset Structure

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Dataset Creation

Curation Rationale

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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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Annotation process

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Bias, Risks, and Limitations

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Recommendations

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Citation [optional]

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Dataset Card Authors [optional]

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Dataset Card Contact

Hanzhi Zhang