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arXiv:2607. 15202v1 Announce Type: new Abstract: Annotation quality is a major bottleneck in building reliable and explainable artificial intelligence (XAI) systems for mental health research.
The study investigates how a large language model, Gemma-3-27B-PT, internally represents depressive symptoms. By applying mechanistic interpretability methods to the model’s residual stream, researchers found that symptom groups are geometrically distinct at layer 21, and that projected symptom vectors align with clinician-annotated rankings across mood, somatic, and suicidality dimensions. Additionally, a single depression vector at this layer can differentiate depressive from non-depressive text with an AUC of 0.789, suggesting a potential emotional valence gate for symptom projection.
arXiv:2606. 10796v1 Announce Type: cross Abstract: Automatic Depression Detection (ADD) from clinical interviews is a pivotal task in computational mental health, yet it remains challenging due to two critical obstacles: 1) difficulty in modeling complex but sparsely distributed depression clues within lengthy, multi-topic clinical interviews, leading to superficial and unreliable reasoning; 2) scarcity of labeled data due to clinical privacy, together with high cost of training and fine-tuning, limiting the deployment of supervised ADD systems.
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.
arXiv:2605.22286v2 Announce Type: replace-cross Abstract: Text-based counseling provides a valuable source of information for assessing depression severity. We study prediction of the total score on...
EviDial is a new benchmark for depression dialogue that separates label agreement from evidence grounding. It contains 382 dialogues and 2,744 participant-symptom cells, each annotated as absent, partial, conflicting, or sufficient evidence. The study finds that most predictions that match benchmark labels lack sufficient textual evidence, highlighting a gap between label accuracy and evidence-based assessment.