The spread of disinformation and propagandistic content poses a threat to
societal harmony, undermining informed decision-making and trust in reliable
sources. Online platforms often serve as breeding grounds for such content, and
malicious actors exploit the vulnerabilities of audiences to shape public
opinion. Although there have been research efforts aimed at the automatic
identification of disinformation and propaganda in social media content, there
remain challenges in terms of performance. The ArAIEval shared task aims to
further research on these particular issues within the context of the Arabic
language. In this paper, we discuss our participation in these shared tasks. We
competed in subtasks 1A and 2A, where our submitted system secured positions
9th and 10th, respectively. Our experiments consist of fine-tuning transformer
models and using zero- and few-shot learning with GPT-4.

在这篇论文中，我们讨论了我们参与阿拉伯语环境下 ArAIEval 共享任务的情况，我们在子任务 1A 和 2A 中分别获得了第 9 和第 10 的名次。我们的实验包括对转换器模型进行微调，并使用 GPT-4 进行零样本和少样本学习。