Can I read Poly-FEVER: A Multilingual Fact Verification Benchmark for Hallucination Detection in Large Language Models on EtoBox?

Poly-FEVER: A Multilingual Fact Verification Benchmark for Hallucination Detection in Large Language Models by Zhang, Hanzhi; Anjum, Sumera; Fan, Heng; Zheng, Weijian; Huang, Yan; Feng, Yunhe is a scholarly article available to read on EtoBox.

What is Poly-FEVER: A Multilingual Fact Verification Benchmark for Hallucination Detection in Large Language Models about?

Hallucinations in generative AI, particularly in Large Language Models (LLMs), pose a significant challenge to the reliability of multilingual applications. Existing benchmarks for hallucination detection focus primarily on English and a few widely spoken languages, lacking the breadth to assess inconsistencies in model performance across diverse linguistic contexts. To address this gap, we introduce Poly-FEVER, a large-scale multilingual fact verification benchmark specifically designed for evaluating hallucination detection in LLMs. Poly-FEVER comprises 77,973 labeled factual claims spanning 11 languages, sourced from FEVER, Climate-FEVER, and SciFact. It provides the first large-scale dataset tailored for analyzing hallucination patterns across languages, enabling systematic evaluation of LLMs such as ChatGPT and the LLaMA series. Our analysis reveals how topic distribution and web resource availability influence hallucination frequency, uncovering language-specific biases that impact model accuracy. By offering a multilingual benchmark for fact verification, Poly-FEVER facilitates cross-linguistic comparisons of hallucination detection and contributes to the development of more

Author

Zhang, Hanzhi; Anjum, Sumera; Fan, Heng; Zheng, Weijian; Huang, Yan; Feng, Yunhe

Published

2025

Language

EN