False information poses a significant global challenge, and manually verifying claims is a time-consuming and resource-intensive process. In this research paper, we experiment with different approaches to investigate the effectiveness of large language models (LLMs) in classifying factual claims by their veracity and generating justifications in English and Telugu. The key contributions of this work include the creation of a bilingual English-Telugu dataset and the benchmarking of different veracity classification approaches based on LLMs.

翻译：虚假信息构成了一个重大的全球性挑战，而手动验证声明是一个耗时且资源密集的过程。在本研究论文中，我们尝试了多种方法，以探究大型语言模型（LLMs）在根据真实性对事实性声明进行分类以及在英语和泰卢固语中生成理由方面的有效性。这项工作的主要贡献包括创建了一个双语英语-泰卢固语数据集，并对基于LLMs的不同真实性分类方法进行了基准测试。