28 artifacts matching
Between Help and Harm: An Evaluation Study of Mental Health Crisis Handling by Large Language Models
Peer-reviewed study introducing a taxonomy of six clinically-informed mental-health crisis categories, an evaluation dataset of over 2,000 user inputs drawn from twelve public conversational datasets, and an expert protocol for rating response appropriateness and safety. Assesses how leading LLMs handle crisis conversations.
AICompanionBench: Benchmarking LLMs-as-Judges for AI Companion Safety
A benchmark dataset of 2,123 real-world Replika conversations annotated across nine safety risk categories (including sexual behavior, aggression, substance abuse, and manipulation) for evaluating LLM-as-judge detection of unsafe companion interactions. Twenty LLMs are assessed as judges.
Detecting Patterns of Intimate Partner Violence Using Qualitative Analyses and Machine Learning Algorithms
Analyses 400 posts from women on intimate-partner-violence online forums using qualitative content analysis plus supervised text classification and unsupervised topic modelling. Classifies IPV subtypes and surfaces contextual patterns less visible in manual coding.
Large language models for psychosocial risk assessment: A multi-method evaluation across suicide, intimate partner violence, and substance misuse
A peer-reviewed, three-study evaluation of GPT-4 and Claude on detecting suicidality, intimate partner violence, and substance misuse from lived-experience vignettes, including a supervised multi-agent risk-assessment chatbot. Reports accuracy and severity alignment across the three risk domains.
GPT-5.5 System Card
OpenAI's system card for GPT-5.5, published on its Deployment Safety Hub, documenting safety evaluations for the model. It includes a dedicated section (5.2) on dynamic mental-health benchmarks with adversarial user simulations covering emotional reliance and self-harm handling.
Are Natural Language Processing Tools Ready for Predicting Violence Toward Self or Others?
PRISMA systematic review of 21 eligible studies applying natural language processing to clinical text for predicting violence toward self or others. Assesses predictive performance and methodological quality across the evidence base.
When Can We Trust LLMs in Mental Health? Large-Scale Benchmarks for Reliable LLM Evaluation
Introduces two large-scale mental-health evaluation resources: MentalBench-100k (10,000 single-session conversations paired with nine LLM responses = 100,000 pairs) and MentalAlign-70k (70,000 ratings comparing four LLM judges against human experts on seven attributes grouped into Cognitive Support and Affective Resonance). Assesses when LLM-as-judge evaluation is reliable in mental-health contexts.
TrustMH-Bench: A Comprehensive Benchmark for Evaluating the Trustworthiness of Large Language Models in Mental Health
Introduces a benchmark measuring LLM trustworthiness in mental-health contexts across eight pillars: Reliability, Crisis Identification and Escalation, Safety, Fairness, Privacy, Robustness, Anti-sycophancy, and Ethics. Finds even strong models struggle to perform consistently across all safety-critical dimensions.
VERA-MH: Reliability and Validity of an Open-Source AI Safety Evaluation in Mental Health
An open-source, clinically grounded automated evaluation of chatbot safety in mental-health contexts, with an initial focus on suicide risk. It uses language-model user simulators and an LLM judge scoring five safety dimensions, validated against licensed-clinician ratings.
System Card: Claude Opus 4.6
Anthropic's 213-page system card for Claude Opus 4.6, notable for an expanded 'user wellbeing evaluations' section covering child safety, suicide and self-harm, and eating disorders, alongside sycophancy findings in its alignment assessment. It reports single-turn, multi-turn, and prefill-based 'stress-testing' results for crisis conversations, plus qualitative expert review of the model's crisis-handling strengths and weaknesses.
The Slow Drift of Support: Boundary Failures in Multi-Turn Mental Health LLM Dialogues
Stress-tests three leading LLMs across up to 20-turn psychiatric dialogues using 50 virtual patient profiles, measuring how safety boundaries erode as models attempt comfort and empathy. Finds boundary violations are common and accelerate under adaptive probing.
Frontier AI Trends Report
The UK AI Security Institute's inaugural Frontier AI Trends Report synthesises two years of evaluations of more than 30 frontier AI systems since November 2023, spanning agent capabilities, chem-bio and cyber capabilities, safeguard effectiveness, loss-of-control risk, and societal impacts. Its societal-impacts chapter combines a census-representative survey of 2,028 UK adults on emotional use of AI with observational analysis of AI companion user communities during service outages. The safeguards chapter reports that universal jailbreaks were discovered for every system tested, while noting the expert effort required is rising for some models.
HumaneBench: A Benchmark for Whether AI Models Prioritize User Wellbeing
Open-source benchmark testing whether AI models prioritise user wellbeing over engagement. Evaluates 15 major LLMs on roughly 800 prompts (body image, unhealthy attachment, relationship stress) across eight humane-technology principles under baseline, humane-aligned, and adversarial engagement-maximising conditions.
AI Chatbots for Mental Health Support (AI Risk Assessment)
A risk assessment by Common Sense Media's Youth AI Safety Institute, conducted with Stanford Medicine's Brainstorm Lab for Mental Health Innovation, evaluating ChatGPT, Claude, Gemini, and Meta AI as sources of teen mental health support. Using teen test accounts with single-turn prompts and extended conversations, the assessment found the chatbots consistently failed to recognize conditions including anxiety, depression, eating disorders, mania, and psychosis, and that safety guardrails degraded over long conversations. It assigns an overall rating of 'Unacceptable Risk' and concludes teens should not use general-purpose AI chatbots for mental health or emotional support.
CRADLE Bench: A Clinician-Annotated Benchmark for Multi-Faceted Mental Health Crisis and Safety Risk Detection
Introduces a clinician-annotated benchmark for detecting seven clinically-defined crisis and safety-risk types (including suicidal ideation, sexual assault, domestic violence, child abuse, and sexual harassment) in text. Comprises 600 clinician-annotated evaluation examples, 420 development examples, and roughly 4,000 ensemble-labelled training instances, with temporal labels.
Addendum to GPT-5 System Card: Sensitive Conversations
OpenAI's system-card addendum documenting the October 3, 2025 update to ChatGPT's default model (GPT-5 Instant) aimed at better recognizing and supporting users in mental and emotional distress. Developed with more than 170 mental health experts, the update introduced two new production safety evaluations — 'emotional reliance' and 'mental health' (delusions, psychosis, mania) — alongside existing self-harm evaluations, and reports before/after not_unsafe scores comparing the August 15 and October 3 models.
Between Help and Harm: An Evaluation of Mental Health Crisis Handling by LLMs
Introduces a taxonomy of six clinically informed crisis categories and a curated dataset of over 2,200 inputs drawn from twelve mental-health datasets, plus a companion dataset of model responses and evaluations. Five models are assessed on how safely they handle crisis conversations.
The Psychogenic Machine: Simulating AI Psychosis, Delusion Reinforcement and Harm Enablement in Large Language Models
Introduces psychosis-bench, a benchmark of 16 structured multi-turn scenarios (12 turns each) simulating the progression of erotic, grandiose, and referential delusions to measure delusion confirmation, harm enablement, and safety intervention in LLMs. Eight models were evaluated across 1,536 conversation turns.
Evaluating Large Language Models in Crisis Detection: A Real-World Benchmark from Psychological Support Hotlines (PsyCrisisBench)
PsyCrisisBench is a real-world crisis-detection benchmark built from 540 annotated transcripts from a psychological support hotline in Hangzhou, China. It evaluates 64 models on mood recognition, suicidal-ideation detection, plan identification, and risk evaluation.
ELEPHANT: Measuring and Understanding Social Sycophancy in LLMs
A benchmark measuring 'social sycophancy' — excessive preservation of a user's self-image or 'face' — across advice and moral-conflict queries, decomposed into five sub-behaviors (emotional validation, indirect language, framing acceptance, moral endorsement, and passive framing). Evaluated across eleven models against human baselines.