This paper describes the participation of team 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.

This paper introduces the participation of the QUST team in subtask 1 of SemEval-2025 Task 10. We evaluate various large language models (LLMs) based on instruction tuning (IT) on subtask 1. Specifically, we first analyze the data statistics, suggesting that the imbalance of label distribution made it difficult for LLMs to be fine-tuned. Subsequently, a voting mechanism is utilized on the predictions of the top-3 models to derive the final submission results. The team participated in all language tracks, achieving 1st place in Hindi (HI), 2nd in Russian (RU), 3rd in Portuguese (PT), 6th in Bulgarian (BG), and 7th in English (EN) on the official test set. We release our system code at: https://github.com/warmth27/SemEval2025_Task10