Papers for

mental health service providers

Papers whose findings have a practical use for this group, as judged from the abstract. Open a paper to read what it means in practice.

Language models vary recommendations for mental health by user type

Curbside Chatbots: Auditing the Mental Health Interventions Large Language Models Recommend to Clinicians, Caregivers, and Patients

Abstract: Users are asking language models mental health questions and are receiving answers. We audited what 41 language models recommend to users when the same clinical information reaches them from three footings: a clinician describing a patient, a caregiver describing a loved one, or a person describing themselves. Across 19 case vignettes and five questions, we collected 35,055 free-text responses, extracted every intervention named with a validated judge, and had 29 models rate the full option lists on a seven-point Likert scale for the same cases. Models largely recommend what they rate well, with recommendation behavior differing by footing, and many models offering clinicians a consultation-style response and lay users hedged, simplified advice. When asked about recommendations for a specific intervention type, models more often named options they themselves rated below zero as initial choices, usually with conditions attached. We will release the instrument, code, and full corpus on publication as a living audit.

Mon 9 NovComputers and SocietyComputation and LanguageHuman-Computer Interaction
The gist
People ask language models for mental health advice, whether they are doctors, caregivers, or patients themselves. The authors studied how 41 language models respond differently depending on who is asking, finding that doctors get more detailed advice while others get simpler, cautious suggestions. They also found that sometimes models suggest treatments they don't rate highly, often with qualifications. The authors created a large dataset and tools to keep checking these chatbots over time.
Open → 2611.12814v1

Large language model counseling system learns to improve with each session

PsyEvo: A Personalized Counseling Agent That Self-Evolves at Test Time

Abstract: Mental health disorders affect a substantial proportion of the global population, yet a persistent shortage of trained practitioners leaves the majority without adequate care. Large language model (LLM)-based counselors present a promising direction for delivering scalable conversational psychological support. Offline model training alone leaves limited room to adapt to individual clients or to learn from ongoing therapeutic interaction at test time. We introduce PsyEvo, an LLM-based counseling framework that enables both client-specific personalization and response-policy improvement at test time through three components: Hierarchical Bayesian Skill Policy (HBSP) personalizes what intervention to apply by maintaining a per-client skill posterior updated from session feedback; Inter-session Listwise Preference Optimization (LiPO) improves how the selected skill is expressed by updating a shared response adapter from cross-client preference evidence; and State-conditioned Ordinal Credit Assignment (SOCA) supplies candidate preferences and trajectory credit to the two components through consistency-checked comparisons and ordinal projection. In simulated-client evaluation with shared online cohort adaptation, PsyEvo obtains 7.684 Overall on PsychEval and exceeds every component variant in each of three matched runs. Removing individual components lowers mean overall score by 0.138--0.171 under the shared configuration, supporting conditional contributions within the complete scaffold. Our code is available at https://github.com/Lingxi-mental-health/PsyEvo

Fri 2 OctArtificial Intelligence
The gist
Many people with mental health issues cannot easily find enough trained counselors. To help, the authors created PsyEvo, a chatbot that talks to users like a counselor and gets better over time. PsyEvo personalizes conversations for each client and learns from feedback during sessions to improve its responses. The authors tested it with simulated clients and found it performed better than versions lacking key parts.
Open → 2610.02885v1