@inproceedings{do-etal-2026-prompt,
    title = "Prompt-Based Modeling of Moments of Change and Change Summaries in Mental Health Timelines",
    author = "Do, Duc  and
      Pham, Tin  and
      Tran, Vu  and
      Nguyen, Minh",
    editor = "Zirikly, Aya  and
      Bar, Kfir  and
      MacAvaney, Sean  and
      Ireland, Molly  and
      Ophir, Yaakov  and
      Atzil-Slonim, Dana  and
      Varadarajan, Vasudha  and
      Bedrick, Steven  and
      Desmet, Bart",
    booktitle = "Proceedings of the 10th Workshop on Computational Linguistics and Clinical Psychology ({CLP}sych 2026)",
    month = jul,
    year = "2026",
    address = "San Diego, California, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl-workshops/2026.clpsych-1.40/",
    pages = "504--509",
    ISBN = "979-8-89176-421-7",
    abstract = "This paper presents our prompt-based approach for modeling mental health timelines from Reddit user posts. We address two tasks: identifying moments of change and generating summaries of clinically meaningful changes across post sequences. Our framework uses large language models with in-context learning to analyze self-states and mental health indicators without task-specific fine-tuning. We build an inference pipeline with vLLM and Qwen2.5-72B-Instruct-GPTQ-Int8, and experiment with few-shot prompting, and balanced few-shot sampling. We also examine how the number of visible posts affects the model{'}s ability to capture temporal changes. Our results suggest that prompt-based methods provide a practical and competitive baseline in low-resource and sensitive mental health settings, particularly for modeling self-state dynamics and generating summaries of psychological change over time."
}