A Comparative Study of Translation Bias and Accuracy in Multilingual Large Language Models for Cross-Language Claim Verification

• URL: http://arxiv.org/abs/2410.10303v1
• Date: Mon, 14 Oct 2024 09:02:42 GMT
• Title: A Comparative Study of Translation Bias and Accuracy in Multilingual Large Language Models for Cross-Language Claim Verification
• Authors: Aryan Singhal, Veronica Shao, Gary Sun, Ryan Ding, Jonathan Lu, Kevin Zhu,
• Abstract summary: This study systematically evaluates translation bias and the effectiveness of Large Language Models for cross-lingual claim verification. We investigate two distinct translation methods: pre-translation and self-translation. Our findings reveal that low-resource languages exhibit significantly lower accuracy in direct inference due to underrepresentation.
• Score: 1.566834021297545
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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