@inproceedings{xu-etal-2025-tinymentalllms,
    title = "{T}iny{M}ental{LLM}s Enable Depression Detection in {C}hinese Social Media Texts",
    author = "Xu, Jinyuan  and
      Lan, Tian  and
      Valette, Mathieu  and
      Magistry, Pierre  and
      Li, Lei",
    editor = "Angelova, Galia  and
      Kunilovskaya, Maria  and
      Escribe, Marie  and
      Mitkov, Ruslan",
    booktitle = "Proceedings of the 15th International Conference on Recent Advances in Natural Language Processing - Natural Language Processing in the Generative AI Era",
    month = sep,
    year = "2025",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd., Shoumen, Bulgaria",
    url = "https://preview.aclanthology.org/corrections-2026-01/2025.ranlp-1.157/",
    pages = "1352--1363",
    abstract = "Depression remains a major global mental health concern, bringing a higher risk of suicide and growing social costs tied to mental disorders. Leveraging social media as a valuable source of emotional signals, we identify two limitations in current NLP-based depression detection frameworks: (1) prediction systems often lack clear, user-friendly explanations for predictions in Depression Detection, and (2) the computational and confidentiality demands of LLMs are misaligned with the need for dependable, privacy-focused small-scale deployments. To address these challenges, we introduce TinyMentalLLMs (TMLs), a compact framework that offers two key contributions: (a) the construction of a small yet representative dataset through psychology-based textometry, and (b) an efficient fine-tuning strategy centered on multiple aspects of depression. This design improves both accuracy and F1 scores in generative models with 0.5B and 1.5B parameters, consistently yielding over 20{\%} performance gains across datasets. TMLs achieve results on par with, and deliver better text quality than, much larger state-of-the-art models."
}