4 citations · 6 across the 5 of their papers we have counts for
5 papers
Instruction Tuning Vs. In-Context Learning: Revisiting Large Language Models in Few-Shot Computational Social Science
Taihang Wang, Xiaoman Xu, Yimin Wang +1
Real-world applications of large language models (LLMs) in computational social science (CSS) tasks primarily depend on the effectiveness of instruction tuning (IT) or in-context l…
Cross-Modal Augmentation for Few-Shot Multimodal Fake News Detection
Ye Jiang, Taihang Wang, Xiaoman Xu +3
The nascent topic of fake news requires automatic detection methods to quickly learn from limited annotated samples. Therefore, the capacity to rapidly acquire proficiency in a new…
Improving the Automated Coronal Jet Identification with U-NET
Jiajia Liu, Chunyu Ji, Yimin Wang +5
Coronal jets are one of the most common eruptive activities in the solar atmosphere. They are related to rich physics processes, including but not limited to magnetic reconnection,…
Team QUST at SemEval-2024 Task 8: A Comprehensive Study of Monolingual and Multilingual Approaches for Detecting AI-generated Text
Xiaoman Xu, Xiangrun Li, Taihang Wang +2
This paper presents the participation of team QUST in Task 8 SemEval 2024. We first performed data augmentation and cleaning on the dataset to enhance model training efficiency and…
A Large-Scale Comparative Study of Accurate COVID-19 Information versus Misinformation
Yida Mu, Ye Jiang, Freddy Heppell +4
The COVID-19 pandemic led to an infodemic where an overwhelming amount of COVID-19 related content was being disseminated at high velocity through social media. This made it challe…