Theory-Explicit Prompting for MIND Self-States: Hierarchical LLMs and Dynamic Signature Extraction in Mental Health Timelines

2026-06-25 · Source: Paper Index on ACL Anthology · Field: Health & Wellbeing — Mental Health & Psychological Support, Medical Devices & Health Technology · Depth: Expert, quick

Summary

A system for the CLPsych 2026 Shared Task on longitudinal mental health modeling from social media timelines is presented, utilizing the MIND framework. This framework defines mental health as evolving self-states across Affect, Behavior, Cognition, and Desire (ABCD). The system employs a theory-explicit prompting framework for structured sequence summarization (Task 3.1) and recurrent dynamic signature extraction (Task 3.2), directly encoding the full ABCD taxonomy into the LLM prompt. Its three-stage pipeline infers a direction-of-change label per sequence, generates structured ABCD summaries with few-shot exemplar augmentation, and aggregates these to derive cross-individual recurrent patterns. The system achieved first rank on deterioration-related recurrent signatures and second overall, securing the top Fit and Specificity scores in Task 3.2, highlighting the advantages of explicit clinical grounding for accuracy.

Key takeaway

For NLP Engineers developing mental health applications, this work demonstrates that explicitly integrating clinical frameworks like MIND into LLM prompts significantly improves model accuracy and interpretability. You should consider encoding established taxonomies directly into your prompting strategies for tasks requiring clinically grounded outputs, especially when modeling complex, longitudinal data. This approach can lead to more reliable and explainable predictions for mental health trajectories.

Key insights

Theory-explicit prompting with the MIND framework enhances LLM accuracy and interpretability in longitudinal mental health modeling.

Principles

• Clinical grounding improves conceptual accuracy.
• ABCD taxonomy provides structured mental health lens.
• Hierarchical LLMs can process complex timelines.

Method

A three-stage pipeline infers direction-of-change, produces structured ABCD summaries via few-shot augmentation, and aggregates them for recurrent pattern extraction.

In practice

• Encode clinical taxonomies directly into LLM prompts.
• Augment summaries with few-shot exemplars.
• Aggregate individual summaries for population patterns.

Topics

• Longitudinal Mental Health
• LLM Prompting
• MIND Framework
• ABCD Taxonomy
• Dynamic Signature Extraction
• Clinical NLP

Best for: AI Scientist, Research Scientist, NLP Engineer

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