@inproceedings{lalor-etal-2024-item,
    title = "Item Response Theory for Natural Language Processing",
    author = "Lalor, John P.  and
      Rodriguez, Pedro  and
      Sedoc, Jo{\~a}o  and
      Hernandez-Orallo, Jose",
    editor = "Mesgar, Mohsen  and
      Lo{\'a}iciga, Sharid",
    booktitle = "Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics: Tutorial Abstracts",
    month = mar,
    year = "2024",
    address = "St. Julian{'}s, Malta",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2024.eacl-tutorials.2",
    pages = "9--13",
    abstract = "This tutorial will introduce the NLP community to Item Response Theory (IRT; Baker 2001). IRT is a method from the field of psychometrics for model and dataset assessment. IRT has been used for decades to build test sets for human subjects and estimate latent characteristics of dataset examples. Recently, there has been an uptick in work applying IRT to tasks in NLP. It is our goal to introduce the wider NLP community to IRT and show its benefits for a number of NLP tasks. From this tutorial, we hope to encourage wider adoption of IRT among NLP researchers.",
}