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.
By Cris Huynh, Arlene Pham
BiGraph-Diffuse is a large‑scale diffusion language model designed for mental health counseling, addressing two key limitations of existing AI dialogue systems: the lack of bidirectional understanding for progressive disclosure and the inadequate use of relational clinical knowledge. It pairs this diffusion model with BiGraph‑RAG, a graph‑structured retrieval approach that uses lightweight entity extraction and semantic linking to preserve inferential pathways from symptoms to underlying causes without incurring LLM token costs during indexing. Experiments and theoretical analysis demonstrate the effectiveness of this mutually reinforcing architecture.
By Yuxiang Cheng, Quanwei Tang, Lvhui Lu, Dong Zhang, Shoushan Li, Erik Cambria
arXiv:2606. 31464v1 Announce Type: cross Abstract: Recent advances in Large Language Models (LLMs) have motivated their adoption across a wide range of domains, including Artificial Intelligence (AI) for mental health.
By Kyomin Hwang, Hyeonjin Kim, Hyunho Lee, Nojun Kwak
arXiv:2606. 10380v1 Announce Type: cross Abstract: Real-world crisis intervention is inherently conversational, yet existing research largely focuses on static texts.
By Grace Byun, Abigail Lott, Rebecca Lipschutz, Sean T. Minton, Elizabeth A. Stinson, Jinho D. Choi
The paper introduces the COmmunity-centered Peer Engaged Support (COPES) dataset and a three‑axis evaluation framework to gauge how well Large Language Models (LLMs) align with community perspectives on mental‑health support queries. Experiments show that fine‑tuning LLMs on COPES improves strategy alignment and emotion‑tone alignment by over 50% for general‑purpose models, yet these gains are uneven across subreddits and coping strategies. The study also finds that post‑training shifts the model’s recommendations toward problem‑focused advice while reducing emotion‑focused responses, indicating persistent disparities in performance across different communities and needs.
By Mohit Chandra, Nabin Kim, Eli Min, Aamogh Sawant, Tanmay Sutar, Munmun De Choudhury
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.
By Fangyi Zhu, Ajay Subramanian, Allison Constant, Camille Wang, Ravish Gupta, Corey J. Keller