@inproceedings{jung-etal-2026-psychometric,
    title = "Do Psychometric Tests Work for Large Language Models? Evaluation of Tests on Sexism, Racism, and Morality",
    author = "Jung, Jana  and
      Lutz, Marlene  and
      Sen, Indira  and
      Strohmaier, Markus",
    editor = "Demberg, Vera  and
      Inui, Kentaro  and
      Marquez, Llu{\'i}s",
    booktitle = "Proceedings of the 19th Conference of the {E}uropean Chapter of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-eacl/2026.eacl-long.380/",
    pages = "8143--8173",
    ISBN = "979-8-89176-380-7",
    abstract = "Psychometric tests are increasingly used to assess psychological constructs in large language models (LLMs). However, it remains unclear whether these tests {--} originally developed for humans {--} yield meaningful results when applied to LLMs. In this study, we systematically evaluate the reliability and validity of human psychometric tests on 17 LLMs for three constructs: sexism, racism, and morality. We find moderate reliability across multiple item and prompt variations. Validity is evaluated through both convergent (i.e., testing theory-based inter-test correlations) and ecological approaches (i.e., testing the alignment between tests scores and behavior in real-world downstream tasks). Crucially, we find that psychometric test scores do not align, and in some cases even negatively correlate with, model behavior in downstream tasks, indicating low ecological validity. Our results highlight that systematic evaluations of psychometric tests on LLMs are essential before interpreting their scores. Our findings also suggest that psychometric tests designed for humans cannot be applied directly to LLMs without adaptation."
}