LongEval at CLEF 2025: Longitudinal Evaluation of IR Model Performance
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arXiv
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| Autores principales: | , , , , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| author | Cancellieri, Matteo El-Ebshihy, Alaa Fink, Tobias Galuščáková, Petra Gonzalez-Saez, Gabriela Goeuriot, Lorraine Iommi, David Keller, Jüri Knoth, Petr Mulhem, Philippe Piroi, Florina Pride, David Schaer, Philipp |
| author_facet | Cancellieri, Matteo El-Ebshihy, Alaa Fink, Tobias Galuščáková, Petra Gonzalez-Saez, Gabriela Goeuriot, Lorraine Iommi, David Keller, Jüri Knoth, Petr Mulhem, Philippe Piroi, Florina Pride, David Schaer, Philipp |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_08541 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | LongEval at CLEF 2025: Longitudinal Evaluation of IR Model Performance Cancellieri, Matteo El-Ebshihy, Alaa Fink, Tobias Galuščáková, Petra Gonzalez-Saez, Gabriela Goeuriot, Lorraine Iommi, David Keller, Jüri Knoth, Petr Mulhem, Philippe Piroi, Florina Pride, David Schaer, Philipp Information Retrieval 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. |
| title | LongEval at CLEF 2025: Longitudinal Evaluation of IR Model Performance |
| topic | Information Retrieval |
| url | https://arxiv.org/abs/2503.08541 |