arXiv AI
No Transformer Beats Six Covariates: Long-Horizon Prediction of Depressive Symptoms from Childhood Essays
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Multilingual Lexical Feature Analysis of Spoken Language for Predicting Major Depression Symptom Severity
arXiv:2511.07011v2 Announce Type: replace-cross Abstract: Background: Remotely captured spoken language could provide objective, regular indicators of depression symptom severity. However, research t...
arXiv AI
Sep 10
EmoTrack: Clinical-Semantic Modeling for Text-Based Depression Severity Estimation
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...
arXiv AI
Aug 10
Natural Language Processing Psychometrics
arXiv:2608. 07316v1 Announce Type: cross Abstract: Natural Language Processing (NLP) models predicting mental health outcomes rarely specify what they measure: contextual knowledge, emotional content, or syntactic structure.
arXiv AI
Sep 3
Interpretable Symptom Vectors for Depression in a Large Language Model
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 Computation and Language
Sep 21
Reading Anxiety or Reading the Label? Comparing Fine-Tuned and Frontier Models for Anxiety Detection on Social Media
The study compares six approaches—frontier commercial models, fine‑tuned smaller models, and conventional classifiers—for detecting anxiety in Reddit posts. It reveals a significant lexical bias: 69.3% of anxiety‑labelled posts contain the word "anxiety" or a variant, allowing models to perform well via keyword matching rather than true language understanding. After removing these terms, the frontier model still leads (F1 = 0.846), but a 110 M‑parameter domain‑adapted encoder achieves a close score (F1 = 0.831) without external API calls, and lexical dependence varies widely across models.
arXiv Computation and Language
Sep 11
"Mirror" Large Language Model Evaluations of Depression are Criterion Contaminated
The study examines how large language models (LLMs) predict depression scores from language responses. In a "Mirror" setup, participants answered structured diagnostic interviews that the LLMs used to predict scores, yielding near-perfect predictions. In a "Non-Mirror" setup, participants gave life history interviews; the LLMs still achieved outstanding prediction accuracy, and both conditions correlated similarly with PHQ-9 scores, indicating that the Mirror advantage disappears when predicting an independent measure. Topic modeling showed different depression themes across interview types, suggesting Mirror evaluations are more about reliability than validity and that Non-Mirror approaches may enhance clinical relevance.