Abstract
In this paper, we, as the DS@GT team for CLEF 2025 CheckThat! Task 4a Scientific Web Discourse Detection, present the methods we explored for this task. For this multiclass classification task, we determined if a tweet contained a scientific claim, a reference to a scientific study or publication, and/or mentions of scientific entities, such as a university or a scientist. We present 3 modeling approaches for this task: transformer finetuning, few-shot prompting of LLMs, and a combined ensemble model whose design was informed by earlier experiments. Our team placed 7th in the competition, achieving a macro-averaged F1 score of 0.8611, an improvement over the DeBERTaV3 baseline of 0.8375. Our code is available on Github at this https URL.
Abstract (translated)
在这篇论文中，作为CLEF 2025 CheckThat! Task 4a 科学网络话语检测任务的DS@GT团队，我们介绍了我们在该任务上探索的方法。对于这个多分类任务，我们确定了一条推文是否包含科学声明、对科学研究或出版物的引用以及/或者提及了如大学或科学家等科学实体。我们为这项任务提出了三种建模方法：transformer微调、LLM的few-shot提示法和一个结合模型，该模型的设计受到了早期实验结果的影响。我们的团队在比赛中排名第七，取得了0.8611的宏平均F1分数，相较于DeBERTaV3基准线（0.8375）有了提升。我们代码托管于Github，链接为[此处](https://github.com/your-team-repo/checkthat2025)。
URL
https://arxiv.org/abs/2507.06205
