arXiv (All)
open access
Probing Warmth-Mediated Harm in Speech-Enabled LLMs for Mental-Health Conversations
Audio LLM benchmarks measure understanding and dialogue quality, not whether speech-enabled models respond with relational warmth when a vulnerable user discloses a mental-health concern. We introduce a 7-turn scripted-disclosure probe grounded in WHO mental-health clinical guidelines, with each scr
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Within-Person Symptom Dynamics in Serious Mental Illness: Insights from Daily Self-reports and Audio Journals#976843
- Within-Person Symptom Dynamics in Serious Mental Illness: Insights from Daily Self-reports and Audio Journals#976753
- From suspicion to violent action: A multi-category framework of fear speech using qualitative content analysis and machine learning#341062
- Positive Ratings, Hidden Concerns: Employee Voice Disclosure in AI-Mediated Organizational Listening#1143389
- Virtual Reality Exposure Therapy for Speech Anxiety Using Commercial Hardware and Software#827803
Record · ID 927556
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive