43 citations · 46 across the 7 of their papers we have counts for
7 papers
Learn to Select: Exploring Label Distribution Divergence for In-Context Demonstration Selection in Text Classification
Ye Jiang, Taihang Wang, Youzheng Liu +3
In-context learning (ICL) for text classification, which uses a few input-label demonstrations to describe a task, has demonstrated impressive performance on large language models…
Team QUST at SemEval-2025 Task 10: Evaluating Large Language Models in Multiclass Multi-label Classification of News Entity Framing
Jiyan Liu, Youzheng Liu, Taihang Wang +3
This paper describes the participation of QUST_NLP in the SemEval-2025 Task 7. We propose a three-stage retrieval framework specifically designed for fact-checked claim retrieval.…
QUST_NLP at SemEval-2025 Task 7: A Three-Stage Retrieval Framework for Monolingual and Crosslingual Fact-Checked Claim Retrieval
Youzheng Liu, Jiyan Liu, Xiaoman Xu +3
This paper describes the participation of QUST_NLP in the SemEval-2025 Task 7. We propose a three-stage retrieval framework specifically designed for fact-checked claim retrieval.…
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…
Large Visual-Language Models Are Also Good Classifiers: A Study of In-Context Multimodal Fake News Detection
Ye Jiang, Yimin Wang
Large visual-language models (LVLMs) exhibit exceptional performance in visual-language reasoning across diverse cross-modal benchmarks. Despite these advances, recent research ind…
Similarity-Aware Multimodal Prompt Learning for Fake News Detection
Ye Jiang, Xiaomin Yu, Yimin Wang +3
The standard paradigm for fake news detection mainly utilizes text information to model the truthfulness of news. However, the discourse of online fake news is typically subtle and…