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Source note3 min read

Self-Evolving Human-Centered Framework for Explainable Depression Symptom Annotation

Source

arXiv

arxiv.org/abs/2607.15202v1

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Source

Plain-Language Brief

Researchers propose a self-evolving annotation framework for Major Depressive Disorder (MDD) that combines large language model (LLM)-assisted labeling with expert verification. The goal is to create explainable, Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, Text Revision (DSM-5-TR) aligned datasets for mental health research. The framework operates in three stages: candidate evidence selection, criterion-level DSM-5-TR analysis, and case-level synthesis. It also includes a dual-memory architecture to internalize expert feedback and improve future annotations.

What the Document Says

  • The text discusses a self-evolving annotation framework for Major Depressive Disorder (MDD).
  • The framework uses large language models (LLM) for labeling with expert verification.
  • The goal is to create explainable, DSM-5-TR aligned datasets for mental health research.
  • The framework operates in three stages: candidate evidence selection, criterion-level DSM-5-TR analysis, and case-level synthesis.
  • A dual-memory architecture is designed to internalize expert feedback and improve future annotations.
  • The framework improves annotation consistency and explainability while reducing manual revision effort in a pilot study.

Key Concepts Mentioned

  • Self-evolving annotation framework
  • Major Depressive Disorder (MDD)
  • Large language model (LLM)
  • DSM-5-TR
  • Expert verification
  • Candidate evidence selection
  • Criterion-level DSM-5-TR analysis
  • Case-level synthesis
  • Dual-memory architecture

Claims Made by the Document

  • The proposed approach improves annotation consistency and explainability.

Evidence Used

  • In a pilot study using expert-reviewed samples, the proposed approach improves annotation consistency and explainability while reducing manual revision effort.

Methods / Approach

  • Large language model (LLM) assisted labeling
  • Expert verification
  • Three stages of annotation: candidate evidence selection, criterion-level DSM-5-TR analysis, and case-level synthesis
  • Dual-memory architecture

Limitations Stated by the Authors

The limitations are not stated in the source.

Limitations Noted by the Agent

Agent interpretation — not from the source.

The evaluation across multiple feedback cycles is left for future work.

Open Questions

  • What specific large language model (LLM) is used?
  • How is the dual-memory architecture designed and implemented?

Notes for Future Q&A

May include interpretation. Labeled where it does.

  • It's not clear from the source which specific large language model (LLM) is being used.
  • Details about the design and implementation of the dual-memory architecture are not provided.

Raw Abstract / Excerpt

Annotation quality is a major bottleneck in building reliable and explainable artificial intelligence (XAI) systems for mental health research. In depression-related datasets, labels are often assigned without structured evidence, symptom-level justification, or traceable alignment with the criteria of the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, Text Revision (DSM-5-TR), limiting both transparency and downstream model interpretability. We propose a self-evolving, expert-in-the-loop annotation framework for Major Depressive Disorder (MDD) that combines large language model (LLM)-assisted labeling with expert verification. The framework is intended to support the construction of explainable, DSM-5-TR-aligned datasets rather than to perform clinical diagnosis. It operates in three stages: candidate evidence selection from textual records, criterion-level DSM-5-TR analysis, and case-level synthesis that produces label-level diagnostic and severity annotations. A dual-memory architecture, composed of Example Memory and Reflection Memory, is designed to internalize expert feedback and iteratively improve future annotations without retraining. We describe this mechanism and leave its evaluation across multiple feedback cycles to future work. In addition to final labels, the framework exports clinical evidence, reasoning traces, and edit histories, enabling comprehensive auditability. In a pilot study using expert-reviewed samples, the proposed approach improves annotation consistency and explainability while reducing manual revision effort.