🧠 AI🔴 Bearish

TrustMH-Bench: A Comprehensive Benchmark for Evaluating the Trustworthiness of Large Language Models in Mental Health

arXiv – CS AI|Zixin Xiong, Ziteng Wang, Haotian Fan, Xinjie Zhang, Wenxuan Wang|March 4, 2026 at 05:00 AM|1 views

🤖AI Summary

Researchers have developed TrustMH-Bench, a comprehensive framework to evaluate the trustworthiness of Large Language Models (LLMs) in mental health applications. Testing revealed that both general-purpose and specialized mental health LLMs, including advanced models like GPT-5.1, significantly underperform across critical trustworthiness dimensions in mental health scenarios.

Key Takeaways

→TrustMH-Bench evaluates LLMs across eight core pillars including reliability, crisis identification, safety, fairness, privacy, robustness, anti-sycophancy, and ethics.
→Extensive testing of six general-purpose and six specialized mental health LLMs revealed significant deficiencies across various trustworthiness dimensions.
→Even advanced models like GPT-5.1 fail to maintain consistently high performance across all trustworthiness dimensions in mental health contexts.
→The research highlights critical gaps in current LLM capabilities for high-stakes, safety-sensitive mental health applications.
→The framework establishes a systematic approach to quantify trustworthiness specifically for mental health AI applications.

#llm #mental-health #ai-safety #trustworthiness #benchmark #evaluation #healthcare-ai #model-testing

Read Original →via arXiv – CS AI

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