@inproceedings{reuben-etal-2025-assessment,
    title = "Assessment and manipulation of latent constructs in pre-trained language models using psychometric scales",
    author = "Reuben, Maor  and
      Slobodin, Ortal  and
      Cohen, Idan-Chaim  and
      Elyashar, Aviad  and
      Braun-Lewensohn, Orna  and
      Cohen, Odeya  and
      Puzis, Rami",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/landing_page/2025.acl-long.121/",
    pages = "2433--2444",
    ISBN = "979-8-89176-251-0",
    abstract = "Human-like personality traits have recently been discovered in large language models, raising the hypothesis that their (known and as yet undiscovered) biases conform with human latent psychological constructs. While large conversational models may be tricked into answering psychometric questionnaires, the latent psychological constructs of thousands of simpler transformers, trained for other tasks, cannot be assessed because appropriate psychometric methods are currently lacking. Here, we show how standard psychological questionnaires can be reformulated into natural language inference prompts, and we provide a code library to support the psychometric assessment of arbitrary models. We demonstrate, using a sample of 88 publicly available models, the existence of human-like mental health-related constructs{---}including anxiety, depression, and the sense of coherence{---}which conform with standard theories in human psychology and show similar correlations and mitigation strategies. The ability to interpret and rectify the performance of language models by using psychological tools can boost the development of more explainable, controllable, and trustworthy models."
}