发表机构
University of North Texas; CheckThat! Lab（北得克萨斯大学; CheckThat!实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对社交媒体科学声明难核实且少链接原始学术来源的问题，提出SciClaimSeekers系统，结合BM25、E5检索及倒数排名融合，经Qwen2.5-14B-Instruct重排，在相关评估中表现良好，证明大型预训练模型组合在该任务中可与微调方法竞争。
AI 中文摘要
科学声明在社交媒体上传播的速度往往快于其被核实的速度，且帖子很少链接到原始学术来源。为解决此问题，本文提出名为SciClaimSeekers的系统，它是一种检索与重排框架，结合BM25和零样本多语言E5检索以及倒数排名融合（k=60），再经Qwen2.5-14B-Instruct逐点重排。在CLEF-2026 CheckThat!任务1评估中，该流程在英文开发集上MRR@5达64.36%，比BM25提升13.67个百分点，比未重排的混合方法提升10.17个百分点，在官方测试集上为64.39%。实验表明，精心组合的大型预训练模型在此任务中可与微调方法竞争。
英文摘要
Scientific claims often spread on social media faster than they can be verified, while posts rarely link to the original scholarly sources. To tackle this problem this paper presents system called SciClaimSeekers, a retrieval and reranking framework by combining BM25 and zero-shot multilingual E5 retrieval with Reciprocal Rank Fusion (k=60), followed by Qwen2.5-14B-Instruct pointwise reranking. The pipeline reaches 64.36% MRR@5 on the English development set a 13.67-point jump over BM25 and 10.17 points over the unranked hybrid and 64.39% on the official test set, in the CLEF-2026 CheckThat! Task 1 evaluation. Our experiment suggests that large pre-trained models, when combined into a careful pipeline, can be competitive with fine-tuned approaches on this task.
CommentsCLEF 2026 Working Notes / CheckThat! Lab at CLEF 2026, Jena, Germany
