Prompt-Based Modeling of Moments of Change and Change Summaries in Mental Health Timelines

2026-06-25 · Source: Paper Index on ACL Anthology · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Mental Health & Psychological Support, Data Science & Analytics · Depth: Advanced, quick

Summary

A prompt-based approach models mental health timelines using Reddit user posts, focusing on identifying moments of change and generating summaries of clinically meaningful changes. This framework employs large language models with in-context learning, specifically Qwen2.5-72B-Instruct-GPTQ-Int8 via vLLM, without requiring task-specific fine-tuning. Researchers experimented with few-shot prompting and balanced few-shot sampling, also examining how the number of visible posts impacts temporal change capture. The results indicate that these prompt-based methods offer a practical and competitive baseline for low-resource and sensitive mental health settings, particularly for analyzing self-state dynamics and summarizing psychological changes over time.

Key takeaway

For NLP Engineers developing mental health applications in low-resource or sensitive environments, you should consider prompt-based large language models as a competitive baseline. This approach, utilizing in-context learning with models like Qwen2.5-72B-Instruct-GPTQ-Int8, allows for effective identification of change moments and generation of clinically meaningful summaries without extensive task-specific fine-tuning, significantly reducing development overhead. Evaluate few-shot prompting strategies and the impact of visible post history on your model's performance.

Key insights

Prompt-based LLMs can effectively model mental health changes and summaries from text without fine-tuning.

Principles

• In-context learning enables LLMs for sensitive tasks.
• Prompt-based methods suit low-resource settings.
• Temporal context affects change detection.

Method

An inference pipeline uses vLLM and Qwen2.5-72B-Instruct-GPTQ-Int8 with few-shot prompting and balanced few-shot sampling to analyze self-states and mental health indicators from post sequences.

In practice

• Analyze self-state dynamics in timelines.
• Summarize psychological changes over time.
• Apply LLMs without task-specific fine-tuning.

Topics

• Mental Health Timelines
• Prompt Engineering
• Large Language Models
• In-Context Learning
• Qwen2.5-72B-Instruct-GPTQ-Int8
• Few-Shot Learning
• Reddit Data Analysis

Best for: AI Engineer, Research Scientist, AI Scientist, NLP Engineer, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Paper Index on ACL Anthology.