针对新闻实体框架多类多标签分类，提出三阶段检索框架提升模型表现。
Team QUST at SemEval-2025 Task 10: Evaluating Large Language Models in Multiclass Multi-label Classification of News Entity Framing
- 分三阶段：先选最优检索模型，再用多重重排序，最后加权投票定结果。
- 在单语赛道获第5名，跨语言赛道获第7名，表现优于多数基线方法。
- 适合关注信息检索与自然语言理解融合应用的研究者参考。
本文介绍了QUST_NLP团队参与SemEval-2025任务7的成果。我们提出一种针对事实核查声明检索的三阶段检索框架。首先评估多种检索模型，选择性能最佳者用于候选集生成；其次采用多个重排序模型对候选结果进行优化，每个模型选取前10项；最后通过加权投票确定最终检索结果。该方法在单语赛道中获得第5名，在跨语言赛道中获得第7名。相关系统代码已公开：https://github.com/warmth27/SemEval2025_Task7。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。