The paper introduces MindEth-Bench, a benchmark designed to assess whether large language models (LLMs) maintain professional ethical boundaries during multi‑turn mental health interactions in China. It uses Monte Carlo Tree Search to generate challenging dialogue trajectories and evaluates nine LLMs, finding that models can shift from appropriate to unethical responses over time. The study demonstrates that single‑turn tests may overestimate ethical reliability, underscoring the need for comprehensive multi‑turn evaluation.
Large language models (LLMs) are increasingly explored for mental health support, yet existing benchmarks primarily focus on supportive capabilities rather than whether models consistently adhere to p...
arXiv:2603. 00048v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) are increasingly deployed in sensitive applications including psychological support, healthcare, and high-stakes decision-making.
arXiv:2607. 10871v1 Announce Type: new Abstract: Contemplative traditions have long guided ethical behavior and prosocial interaction, and recent work suggests that contemplative principles (e.
arXiv:2601.13235v2 Announce Type: replace-cross Abstract: Caregivers seeking AI-mediated support express complex needs---information-seeking, emotional validation, and distress cues---that warrant ca...
PsyCLIENT is a framework that uses large language models to simulate counseling clients by conditioning responses on client profiles, dialogue histories, and conversational trajectories that specify target behaviors and content constraints. The authors created PsyCLIENT-CP, a dataset of 120 Chinese client profiles covering 60 counseling topics, and evaluated the system with 24 professional counselors, finding higher authenticity and training utility ratings compared to baseline methods. In a source-identification study, PsyCLIENT-generated dialogues were more often mistaken for real human-client interactions than those from other models, indicating the effectiveness of conversational trajectory modeling in producing realistic counseling conversations.
VEILED‑SH is a new benchmark for evaluating AI responses to indirect expressions of suicide, self‑harm, and distress. It contains 10,003 prompts drawn from counselor chats, forum posts, platform searches, and triage, all grounded in Singaporean contexts and public datasets. The dataset uses an expert‑validated multi‑label taxonomy to classify intent, communicative function, and response behavior, and it includes six automated profiles calibrated to peer‑support practitioners and clinicians for proxy‑alignment scoring.
APRiL is an open‑ended benchmark designed to evaluate Chinese large language models’ social awareness. It comprises 2,196 evaluation instances across six categories, split into two tasks: Social Scenario Analysis, which tests understanding of social cues, mental states, and conflict structures, and Socially Aware Response, which checks whether models can generate appropriate, natural replies. Experiments on twelve models from six families show a consistent gap between understanding and response quality, indicating that stronger scenario analyses tend to improve response performance.
MindEval2 is a new evaluation framework for AI chatbots in mental health support, comprising Mental Health Quality Rubrics (MHQR) and the MindSim patient simulation harness. MHQR, developed with PhD-level psychologists, provides explicit, actionable criteria for assessing response quality, while MindSim offers realistic, longitudinal patient interactions with a memory component. The study evaluated 18 models, finding that although surface-level quality has improved, guidance calibration remains the main failure across all models.
How can we evaluate whether frontier AI systems recognize child-safety risks before they escalate into explicit harm? Existing child safety evaluations focus on child sexual abuse material, yet many child-safety failures begin earlier: in model assistance that helps adults manipulate, impersonate, profile, or isolate minors, and in model responses that deepen children's emotional dependence on AI systems rather than redirecting them toward human support.
arXiv:2509.04183v3 Announce Type: replace-cross Abstract: The growing demand for scalable psychological counseling highlights the need for high-quality, privacy-compliant data, yet such data remains...
arXiv:2609.15855v1 Announce Type: cross Abstract: % !TEX root = ../main.tex People increasingly use large language models (LLMs) for mental health support, yet their safety in evolving, high-risk con...
arXiv:2608. 08061v1 Announce Type: new Abstract: The key question in moral judgement is not simply whether someone chooses the "right" answer, but how they decide what matters most when moral principles conflict.