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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
  • *Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review

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.

Original languageEnglish
Pages (from-to)2267-2289
Number of pages23
JournalCEUR Workshop Proceedings
Volume3740
StatePublished - 2024
Event25th Working Notes of the Conference and Labs of the Evaluation Forum, CLEF 2024 - Grenoble, France
Duration: 9 Sep 202412 Sep 2024

Keywords

  • Evaluation
  • Information Retrieval
  • Temporal Generalisability
  • Temporal Persistence
  • Text Classification

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