用大模型自动核查英语和泰卢固语事实，提升多语言信息真伪判断效率。
Automatic Fact-checking in English and Telugu
- 基于大模型构建双语事实核查框架，支持英文与泰卢固语
- 创建首个英-泰卢固语双语事实核查数据集
- 验证了大模型在跨语言事实分类中的可行性，适合多语言NLP研究者
虚假信息构成全球性挑战，人工核实耗时费力。本文探索不同方法，评估大语言模型（LLMs）在英语和泰卢固语中对事实性陈述的真伪分类能力，并生成解释性理由。主要贡献包括构建首个英-泰卢固语双语事实核查数据集，以及基于LLMs的真伪分类方法基准测试。实验表明，大模型在跨语言事实核查任务中具有潜力，为多语言信息真实性验证提供了新路径。
原文摘要 · Abstract (English)
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.
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