LLUMI: Improving LLM Writing Assistance for Mental Health Support with Online Community Feedback
Large language models (LLMs) show promise in generating supportive responses for mental health queries, but improving their usefulness, empathy, and safety often requires substantial compute, expert input, and labeled data. At the same time, deploying proprietary, cloud-based models for mental health-related interactions raises important privacy and data-governance concerns, given the sensitivities. To address this challenge, we introduce LLUMI setup that can be hosted in-house within protected
Record details
Published: 28 May 2026
Source: arXiv
Category: Research
Topics: Regulation · Privacy · Healthcare
Retrieved: 14 July 2026
Related evidence
These records share source-supplied organisations, an exact publisher byline, automatic topics or regions. The reason is shown on every link; related does not mean supporting, agreeing with or verifying this record.
Towards an automated AI-based framework for floor plan compliance checks for residential buildings
arXiv · 26 May 2026
PySynthea: A Python-Native Framework for Scalable Synthetic Healthcare Data Generation
arXiv · 2 June 2026
POLAR-Bench: A Diagnostic Benchmark for Privacy-Utility Trade-offs in LLM Agents
arXiv · 18 May 2026
BiFedKD: Bidirectional Federated Knowledge Distillation Framework for Non-IID and Long-Tailed ECG Monitoring
arXiv · 14 May 2026
"The New Era of Tech-Enabled Traceability": Tensions between the FDA's Data Governance Vision and the Lived Realities of Food Producers
arXiv · 17 June 2026
When RAG Chatbots Expose Their Backend: An Anonymized Case Study of Privacy and Security Risks in Patient-Facing Medical AI
arXiv · 1 May 2026
How to cite this record
ethics.ai (28 May 2026), “LLUMI: Improving LLM Writing Assistance for Mental Health Support with Online Community Feedback,” evidence record 3488, https://ethics.ai/record/3488 (originally published by arXiv).
Use and limitations
This page is a stable index and citation surface for a source record. ethics.ai did not author the underlying report and has not independently verified every claim. Automatic topics may be imperfect. For consequential use, quote and cite the original publisher.