The rise of digital misinformation has heightened interest in using multilingual Large Language Models (LLMs) for fact-checking. This study systematically evaluates translation bias and the effectiveness of LLMs for cross-lingual claim verification across 15 languages from five language families: Romance, Slavic, Turkic, Indo-Aryan, and Kartvelian. Using the XFACT dataset to assess their impact on accuracy and bias, we investigate two distinct translation methods: pre-translation and self-translation. We use mBERT's performance on the English dataset as a baseline to compare language-specific accuracies. Our findings reveal that low-resource languages exhibit significantly lower accuracy in direct inference due to underrepresentation in the training data. Furthermore, larger models demonstrate superior performance in self-translation, improving translation accuracy and reducing bias. These results highlight the need for balanced multilingual training, especially in low-resource languages, to promote equitable access to reliable fact-checking tools and minimize the risk of spreading misinformation in different linguistic contexts.

本研究针对数字虚假信息的传播现象，探讨了多语言大型语言模型在跨语言主张验证中的翻译偏差与准确性问题。通过比较预翻译和自我翻译两种方法，研究发现低资源语言的准确性较低，然而更大的模型在自我翻译中的表现更佳，从而提升了翻译的准确性并减少偏差，强调了在低资源语言中均衡多语种训练的重要性。

跨语言主张验证中多语种大型语言模型的翻译偏差与准确性的比较研究