Beyond Translation: LLM-Based Data Generation for Multilingual Fact-Checking

Yi-Ling Chung; Aurora Cobo; Pablo Serna

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arXiv · 2502.15419

Beyond Translation: LLM-Based Data Generation for Multilingual Fact-Checking

Yi-Ling Chung·Aurora Cobo·Pablo Serna

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Abstract

Robust automatic fact-checking systems have the potential to combat online misinformation at scale. However, most existing research primarily focuses on English. In this paper, we introduce MultiSynFact, the first large-scale multilingual fact-checking dataset containing 2.2M claim-source pairs designed to support Spanish, German, English, and other low-resource languages. Our dataset generation pipeline leverages Large Language Models (LLMs), integrating external knowledge from Wikipedia and incorporating rigorous claim validation steps to ensure data quality. We evaluate the effectiveness of MultiSynFact across multiple models and experimental settings. Additionally, we open-source a user-friendly framework to facilitate further research in multilingual fact-checking and dataset generation.

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Yi-Ling Chung, Aurora Cobo, Pablo Serna. 2025-02-21. Beyond Translation: LLM-Based Data Generation for Multilingual Fact-Checking. https://arxiv.org/abs/2502.15419

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