@inproceedings{yunusov-etal-2025-personality,
    title = "Personality Matters: User Traits Predict {LLM} Preferences in Multi-Turn Collaborative Tasks",
    author = "Yunusov, Sarfaroz  and
      Chen, Kaige  and
      Anwar, Kazi Nishat  and
      Emami, Ali",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/name-variant-enfa-fane/2025.emnlp-main.71/",
    doi = "10.18653/v1/2025.emnlp-main.71",
    pages = "1359--1372",
    ISBN = "979-8-89176-332-6",
    abstract = "As Large Language Models (LLMs) increasingly integrate into everyday workflows, where users shape outcomes through multi-turn collaboration, a critical question emerges: do users with different personality traits systematically prefer certain LLMs over others? We conduc-ted a study with 32 participants evenly distributed across four Keirsey personality types, evaluating their interactions with GPT-4 and Claude 3.5 across four collaborative tasks: data analysis, creative writing, information retrieval, and writing assistance. Results revealed significant personality-driven preferences: *Rationals* strongly preferred GPT-4, particularly for goal-oriented tasks, while *idealists* favored Claude 3.5, especially for creative and analytical tasks. Other personality types showed task-dependent preferences. Sentiment analysis of qualitative feedback confirmed these patterns. Notably, aggregate helpfulness ratings were similar across models, showing how personality-based analysis reveals LLM differences that traditional evaluations miss."
}