3 papers
cs.CL2025
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…
cs.CL2025
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.…
cs.IR2025
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.…