@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://aclanthology.org/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."
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<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.</abstract>
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%0 Conference Proceedings
%T Prompt-Based Modeling of Moments of Change and Change Summaries in Mental Health Timelines
%A Do, Duc
%A Pham, Tin
%A Tran, Vu
%A Nguyen, Minh
%Y Zirikly, Aya
%Y Bar, Kfir
%Y MacAvaney, Sean
%Y Ireland, Molly
%Y Ophir, Yaakov
%Y Atzil-Slonim, Dana
%Y Varadarajan, Vasudha
%Y Bedrick, Steven
%Y Desmet, Bart
%S Proceedings of the 10th Workshop on Computational Linguistics and Clinical Psychology (CLPsych 2026)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, USA
%@ 979-8-89176-421-7
%F do-etal-2026-prompt
%X 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.
%U https://aclanthology.org/2026.clpsych-1.40/
%P 504-509
Markdown (Informal)
[Prompt-Based Modeling of Moments of Change and Change Summaries in Mental Health Timelines](https://aclanthology.org/2026.clpsych-1.40/) (Do et al., CLPsych 2026)
ACL