LongEval: Longitudinal Evaluation of Model Performance at CLEF 2023

Rabab Alkhalifa, Iman Bilal, Hsuvas Borkakoty, Jose Camacho-Collados, Romain Deveaud, Alaa El-Ebshihy, Luis Espinosa-Anke, Gabriela Gonzalez-Saez, Petra GalušÄáková, Lorraine Goeuriot, Elena Kochkina, Maria Liakata, Daniel Loureiro, Harish Tayyar Madabushi, Philippe Mulhem, Florina Piroi, Martin Popel, Christophe Servan, Arkaitz Zubiaga

ECIR. 2023.

In this paper, we describe the plans for the first LongEval CLEF 2023 shared task dedicated to evaluating the temporal persistence of Information Retrieval (IR) systems and Text Classifiers. The task is motivated by recent research showing that the performance of these models drops as the test data becomes more distant, with respect to time, from the training data. LongEval differs from traditional shared IR and classification tasks by giving special consideration to evaluating models aiming to mitigate performance drop over time. We envisage that this task will draw attention from the IR community and NLP researchers to the problem of temporal persistence of models, what enables or prevents it, potential solutions and their limitations.

@inproceedings{alkhalifa2023longeval,
  title={LongEval: Longitudinal Evaluation of Model Performance at CLEF 2023},
  author={Alkhalifa, Rabab and Bilal, Iman and Borkakoty, Hsuvas and Camacho-Collados, Jose and Deveaud, Romain and El-Ebshihy, Alaa and Espinosa-Anke, Luis and Gonzalez-Saez, Gabriela and Galu{\v{s}}{\v{c}}{\'a}kov{\'a}, Petra and Goeuriot, Lorraine and others},
  booktitle={European Conference on Information Retrieval},
  pages={499--505},
  year={2023},
  organization={Springer}
}