{
  "abstract": "Evaluating large language models (LLMs) in the mental health domain presents distinct challenges due to the subtle, context-dependent, and subjective nature of psychological symptoms. We introduce PsyEval, a benchmark specifically designed to evaluate LLMs in mental health-related tasks across three core dimensions: knowledge, diagnosis, and emotional support. PsyEval is constructed to reflect the complexity of mental health scenarios and provides a structured framework for assessing model performance within this sensitive domain. Using PsyEval, we evaluate eleven advanced LLMs with different prompting strategies to investigate how prompting affects their responses. The results reveal considerable gaps in LLMs' current ability to reason accurately and respond appropriately in mental health contexts, while also indicating promising directions for future model enhancement.",
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  "affiliations": [
    "X-LANCE Lab, School of Computer Science, Shanghai Jiao Tong University, Shanghai, China",
    "Department of Human Anatomy, Histology and Embryology, Peking University Health Science Center, Beijing, China",
    "Child Healthcare Department, Shanghai Children's Hospital, Shanghai, China",
    "ACL Lab, University of Texas at Arlington, Arlington, TX, USA. kenny.zhu@uta.edu",
    "X-LANCE Lab, School of Computer Science, Shanghai Jiao Tong University, Shanghai, China. mengyuewu@sjtu.edu.cn"
  ],
  "article_type": "research article",
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  "author_total": 6,
  "authors": [
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      "name": "Haoan Jin"
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      "name": "Siyuan Chen",
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      "name": "Dilawaier Dilixiati"
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      "name": "Yewei Jiang"
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      "name": "Kenny Q. Zhu"
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      "name": "Mengyue Wu"
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  "bibtex": "https://research.draconitic.com/paper/5c1bkaxsm2wew.bib",
  "cited_by": 43,
  "doi": "10.1038/s44184-026-00227-0",
  "fulltext_url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC13728106/",
  "id": "5c1bkaxsm2wew",
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  "pdf_url": "https://www.nature.com/articles/s44184-026-00227-0_reference.pdf",
  "pmcid": "PMC13728106",
  "pmid": "42426307",
  "published": "2026-07-09",
  "publisher": "Springer Science+Business Media",
  "ris": "https://research.draconitic.com/paper/5c1bkaxsm2wew.ris",
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  "subjects": [
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      "field": "medicine",
      "origin": "openalex:2718|Artificial Intelligence in Healthcare and Education",
      "subfield": "epidemiology-public-health"
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  "title": "PsyEval: a comprehensive large language model evaluation benchmark for mental health",
  "title_text": "PsyEval: a comprehensive large language model evaluation benchmark for mental health",
  "type": "peer-reviewed",
  "updated_at": "2026-11-08T13:20:50Z",
  "url": "https://research.draconitic.com/paper/5c1bkaxsm2wew",
  "venue": "npj Mental Health Research",
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