@inproceedings{bhandarkar-etal-2025-psytex,
    title = "{P}sy{TE}x: A Knowledge-Guided Approach to Refining Text for Psychological Analysis",
    author = "Bhandarkar, Avanti  and
      Wilson, Ronald  and
      Swarup, Anushka  and
      Webster, Gregory  and
      Woodard, Damon",
    editor = {H{\"a}m{\"a}l{\"a}inen, Mika  and
      {\"O}hman, Emily  and
      Bizzoni, Yuri  and
      Miyagawa, So  and
      Alnajjar, Khalid},
    booktitle = "Proceedings of the 5th International Conference on Natural Language Processing for Digital Humanities",
    month = may,
    year = "2025",
    address = "Albuquerque, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/Ingest-2025-COMPUTEL/2025.nlp4dh-1.14/",
    pages = "151--178",
    ISBN = "979-8-89176-234-3",
    abstract = "LLMs are increasingly applied for tasks requiring deep interpretive abilities and psychological insights, such as identity profiling, mental health diagnostics, personalized content curation, and human resource management. However, their performance in these tasks remains inconsistent, as these characteristics are not explicitly perceptible in the text. To address this challenge, this paper introduces a novel protocol called the {\textquotedblleft}Psychological Text Extraction and Refinement Framework (PsyTEx){\textquotedblright} that leverages LLMs to isolate and amplify psychologically informative segments and evaluate LLM proficiency in interpreting complex psychological constructs from text. Using personality recognition as a case study, our extensive evaluation of five SOTA LLMs across two personality models (Big Five and Dark Triad) and two assessment levels (detection and prediction) highlights significant limitations in LLM`s ability to accurately interpret psychological traits. However, our findings show that LLMs, when used within the PsyTEx protocol, can effectively extract relevant information that closely aligns with psychological expectations, offering a structured approach to support future advancements in modeling, taxonomy construction, and text-based psychological evaluations."
}