In this paper, we highlight our approach for the "Arabic AI Tasks Evaluation
(ArAiEval) Shared Task 2023". We present our approaches for task 1-A and task
2-A of the shared task which focus on persuasion technique detection and
disinformation detection respectively. Detection of persuasion techniques and
disinformation has become imperative to avoid distortion of authentic
information. The tasks use multigenre snippets of tweets and news articles for
the given binary classification problem. We experiment with several
transformer-based models that were pre-trained on the Arabic language. We
fine-tune these state-of-the-art models on the provided dataset. Ensembling is
employed to enhance the performance of the systems. We achieved a micro
F1-score of 0.742 on task 1-A (8th rank on the leaderboard) and 0.901 on task
2-A (7th rank on the leaderboard) respectively.

我们在本文中重点介绍了我们为《2023 年阿拉伯语 AI 任务评估（ArAiEval）共享任务》开展的方法。我们提出了针对共享任务的任务 1-A 和任务 2-A 的方法，重点是说服技巧检测和虚假信息检测。检测说服技巧和虚假信息已成为避免真实信息扭曲的必要手段。该任务使用多种体裁的推文和新闻文章来进行给定的二元分类问题。我们尝试了在阿拉伯语上预训练的几种基于 Transformer 的模型，并在提供的数据集上进行了微调。我们采用集成方法来提高系统的性能。我们在任务 1-A 上获得了 0.742 的微平均 F1 分数（排行榜第 8），在任务 2-A 上获得了 0.901 的微平均 F1 分数（排行榜第 7）。

ArAIEval 共享任务中的 MAVERICKS：构建更安全的数字空间 -- 转换器集成模型解决欺骗与说服

Mavericks at ArAIEval Shared Task: Towards a Safer Digital Space --  Transformer Ensemble Models Tackling Deception and Persuasion

We present an overview of the ArAIEval shared task, organized as part of the
first ArabicNLP 2023 conference co-located with EMNLP 2023. ArAIEval offers two
tasks over Arabic text: (i) persuasion technique detection, focusing on
identifying persuasion techniques in tweets and news articles, and (ii)
disinformation detection in binary and multiclass setups over tweets. A total
of 20 teams participated in the final evaluation phase, with 14 and 16 teams
participating in Tasks 1 and 2, respectively. Across both tasks, we observed
that fine-tuning transformer models such as AraBERT was at the core of the
majority of the participating systems. We provide a description of the task
setup, including a description of the dataset construction and the evaluation
setup. We further give a brief overview of the participating systems. All
datasets and evaluation scripts from the shared task are released to the
research community. (this https URL) We hope this will enable
further research on these important tasks in Arabic.

介绍了 ArAIEval 共享任务的概述，该任务主要包括通过检测推文和新闻文章中的说服技巧来识别说服技巧，并通过推文中的二进制和多类别设置来检测虚假信息。

ArAIEval 共享任务：阿拉伯语文本中的说服技巧和虚假信息检测

ArAIEval Shared Task: Persuasion Techniques and Disinformation Detection  in Arabic Text

The advent of generative Large Language Models (LLMs) such as ChatGPT has
catalyzed transformative advancements across multiple domains. However,
alongside these advancements, they have also introduced potential threats. One
critical concern is the misuse of LLMs by disinformation spreaders, leveraging
these models to generate highly persuasive yet misleading content that
challenges the disinformation detection system. This work aims to address this
issue by answering three research questions: (1) To what extent can the current
disinformation detection technique reliably detect LLM-generated
disinformation? (2) If traditional techniques prove less effective, can LLMs
themself be exploited to serve as a robust defense against advanced
disinformation? and, (3) Should both these strategies falter, what novel
approaches can be proposed to counter this burgeoning threat effectively? A
holistic exploration for the formation and detection of disinformation is
conducted to foster this line of research.

通过回答三个研究问题，本研究探讨了利用大型语言模型（LLMs）生成的谣言和虚假信息对目前存在的谣言检测技术的影响，以及通过使用 LLMs 作为强大的谣言防御手段以及针对这一威胁的新方法的可能性。