@inproceedings{liu-etal-2025-eeyore,
    title = "Eeyore: Realistic Depression Simulation via Expert-in-the-Loop Supervised and Preference Optimization",
    author = "Liu, Siyang  and
      Brie, Bianca  and
      Li, Wenda  and
      Biester, Laura  and
      Lee, Andrew  and
      Pennebaker, James  and
      Mihalcea, Rada",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/transition-to-people-yaml/2025.findings-acl.707/",
    doi = "10.18653/v1/2025.findings-acl.707",
    pages = "13750--13770",
    ISBN = "979-8-89176-256-5",
    abstract = "Large Language Models (LLMs) have been previously explored for mental healthcare training and therapy client simulation, but they still fall short in authentically capturing diverse client traits and psychological conditions. We introduce \textbf{Eeyore} , an 8B model optimized for realistic depression simulation through a structured alignment framework, incorporating expert input at every stage.First, we systematically curate real-world depression-related conversations, extracting depressive traits to guide data filtering and psychological profile construction, and use this dataset to instruction-tune Eeyore for profile adherence. Next, to further enhance realism, Eeyore undergoes iterative preference optimization{---}first leveraging model-generated preferences and then calibrating with a small set of expert-annotated preferences.Throughout the entire pipeline, we actively collaborate with domain experts, developing interactive interfaces to validate trait extraction and iteratively refine structured psychological profiles for clinically meaningful role-play customization.Despite its smaller model size, the Eeyore depression simulation outperforms GPT-4o with SOTA prompting strategies, both in linguistic authenticity and profile adherence."
}