1 citations · 3 across the 7 of their papers we have counts for
5 papers · 1 filter
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.…
AMPLE: Emotion-Aware Multimodal Fusion Prompt Learning for Fake News Detection
Xiaoman Xu, Xiangrun Li, Taihang Wang +1
Detecting fake news in large datasets is challenging due to its diversity and complexity, with traditional approaches often focusing on textual features while underutilizing semant…
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…
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…