Team QUST at SemEval-2025 Task 10: Evaluating Large Language Models in Multiclass Multi-label Classification of News Entity Framing
This work addresses the challenge of retrieving fact-checked claims for researchers and practitioners in NLP, but it is incremental as it builds on existing retrieval and re-ranking methods.
The paper tackled the problem of fact-checked claim retrieval by proposing a three-stage retrieval framework, achieving 5th place in the monolingual track and 7th place in the crosslingual track in SemEval-2025 Task 7.
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. Initially, we evaluate the performance of several retrieval models and select the one that yields the best results for candidate retrieval. Next, we employ multiple re-ranking models to enhance the candidate results, with each model selecting the Top-10 outcomes. In the final stage, we utilize weighted voting to determine the final retrieval outcomes. Our approach achieved 5th place in the monolingual track and 7th place in the crosslingual track. We release our system code at: https://github.com/warmth27/SemEval2025_Task7.