![Cover Image for System.Linq.Enumerable+EnumerablePartition`1[System.Char]](https://coverimages.igi-global.com/images/search-article.png)
Extended overview of the CLEF 2024 LongEval Lab on Longitudinal Evaluation of Model Performance
Rabab Alkhalifa, Hsuvas Borkakoty, Romain Deveaud, Alaa El-Ebshihy, Luis Espinosa-Anke, Tobias Fink, Petra Galuščáková, Gabriela Gonzalez-Saez, Lorraine Goeuriot, David Iommi, Maria Liakata, Harish Tayyar Madabushi, Pablo Medina-Alias, Philippe Mulhem, Florina Piroi, Martin Popel, Arkaitz Zubiaga
OAI: oai:purehost.bath.ac.uk:publications/27856b44-97f9-4a84-bf9c-66744ab74618
Abstract
We describe the second edition of the LongEval CLEF 2024 shared task. This lab evaluates the temporal persistence of Information Retrieval (IR) systems and Text Classifiers. Task 1 requires IR systems to run on corpora acquired at several timestamps, and evaluates the drop in system quality (NDCG) along these timestamps. Task 2 tackles binary sentiment classification at different points in time, and evaluates the performance drop for different temporal gaps. Overall, 37 teams registered for Task 1 and 25 for Task 2. Ultimately, 14 and 4 teams participated in Task 1 and Task 2, respectively.