评测检索模型随时间退化，推动自适应系统发展
LongEval at CLEF 2025: Longitudinal Evaluation of IR Model Performance
- 设计双任务数据集，模拟查询与文档相关性随时间变化
- 发现模型性能随训练与测试时间间隔增大而显著下降
- 适合关注长时序检索与系统鲁棒性的研究者
本文介绍了第十三届CLEF 2025会议中的LongEval实验室第三版，持续探索信息检索（IR）中的时间持久性挑战。该实验室设置两项任务，为研究者提供反映用户查询与文档相关性随时间演变的测试数据。通过评估模型性能在训练数据与测试数据时间偏离下的退化情况，LongEval旨在深化对IR系统时间动态的理解。2025年版本旨在推动信息检索与自然语言处理领域共同应对自适应模型开发，以在网页搜索和科学检索领域保持长期检索质量。
原文摘要 · Abstract (English)
This paper presents the third edition of the LongEval Lab, part of the CLEF 2025 conference, which continues to explore the challenges of temporal persistence in Information Retrieval (IR). The lab features two tasks designed to provide researchers with test data that reflect the evolving nature of user queries and document relevance over time. By evaluating how model performance degrades as test data diverge temporally from training data, LongEval seeks to advance the understanding of temporal dynamics in IR systems. The 2025 edition aims to engage the IR and NLP communities in addressing the development of adaptive models that can maintain retrieval quality over time in the domains of web search and scientific retrieval.
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