超越翻译：面向多语言事实核查的基于大语言模型的数据生成
Beyond Translation: LLM-Based Data Generation for Multilingual Fact-Checking
AI总结：
针对现有事实核查研究多聚焦英语的问题，本文提出含220万条多语言声明-来源对的MultiSynFact数据集，通过结合LLM、维基百科知识与严格验证步骤生成数据，验证其有效性并开源易用框架以推动相关研究。
摘要：
鲁棒的自动事实核查系统有望大规模打击网络错误信息，但现有研究大多主要聚焦英语场景。本文提出MultiSynFact，这是首个大规模多语言事实核查数据集，包含220万条声明-来源对，覆盖西班牙语、德语、英语及其他低资源语言。其数据生成流水线利用大语言模型（LLMs），整合来自Wikipedia的外部知识，并加入严格的声明验证步骤以保障数据质量。我们在多个模型和实验设置下验证了MultiSynFact的有效性，还开源了一个易用框架，以推动多语言事实核查与数据集生成领域的后续研究。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
