跨语言学习与低资源微调：以土耳其语事实核查为例
Cross-Lingual Learning vs. Low-Resource Fine-Tuning: A Case Study with Fact-Checking in Turkish
AI总结：
针对土耳其语事实核查数据集稀缺问题，研究构建了包含3238条声明的FCTR数据集，并评估了跨语言迁移学习及大模型上下文学习在低资源土耳其语事实核查中的有效性。
摘要：
错误信息通过社交媒体平台的快速传播引发了人们对其影响公众舆论的担忧。尽管错误信息在其他语言中也很普遍，但该领域的大部分研究都集中在英语上。因此，包括土耳其语在内的其他语言的数据集十分稀缺。为了解决这一问题，我们引入了 FCTR 数据集，包含 3238 条真实世界的声明。该数据集涵盖多个领域，并整合了从三个土耳其事实核查组织收集的证据。此外，我们旨在评估跨语言迁移学习对低资源语言（特别是土耳其语）的有效性。我们在此背景下展示了大型语言模型的上下文学习（零样本和少样本）性能。实验结果表明，该数据集具有推进土耳其语研究的潜力。
英文摘要：
The rapid spread of misinformation through social media platforms has raised concerns regarding its impact on public opinion. While misinformation is prevalent in other languages, the majority of research in this field has concentrated on the English language. Hence, there is a scarcity of datasets for other languages, including Turkish. To address this concern, we have introduced the FCTR dataset, consisting of 3238 real-world claims. This dataset spans multiple domains and incorporates evidence collected from three Turkish fact-checking organizations. Additionally, we aim to assess the effectiveness of cross-lingual transfer learning for low-resource languages, with a particular focus on Turkish. We demonstrate in-context learning (zero-shot and few-shot) performance of large language models in this context. The experimental results indicate that the dataset has the potential to advance research in the Turkish language.
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。
