arXiv:2602. 18443v2 Announce Type: replace-cross Abstract: Psychosocial online counselling frequently encounters generic subject lines that impede efficient case prioritisation.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.
arXiv:2608.22251v1 Announce Type: cross Abstract: Rising global demand for mental health support creates significant service delivery challenges, with asynchronous email counselling serving as a cruc...
arXiv:2510.25384v2 Announce Type: replace Abstract: Large Language Models (LLMs) are promising tools for synthetic data generation in mental health. However, privacy policies and restrictions forced...
arXiv:2606. 23884v1 Announce Type: cross Abstract: General-purpose large language models (LLMs) are increasingly used for mental health-related conversations, yet safety safeguards remain inadequate and inconsistent across clinical conditions.
arXiv:2602. 05088v4 Announce Type: replace Abstract: Millions of people now use generative AI chatbots for psychological support.
arXiv:2605.30273v2 Announce Type: replace-cross Abstract: Large language models (LLMs) show promise in generating supportive responses for mental health queries, but improving their usefulness, empat...
HealthBench-Psych is a mental‑health subset of the OpenAI HealthBench benchmark, created by filtering 5,000 physician‑rubric conversations for mental‑health relevance using an LLM‑applied rubric and validating the selection through two rounds of blinded clinician review. The resulting 610 conversations (12.2 % of the corpus) are released along with a pipeline, model responses, grades, and analysis code. Evaluation of 20 frontier and open models by a cross‑vendor panel of three LLM judges shows a statistically tied frontier cluster, measurable refusal behavior in two models, and near‑identical rankings across judges (τ ≥ 0.92).