arXiv AI
LLUMI: Improving LLM Writing Assistance for Mental Health Support with Online Community Feedback
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arXiv AI
Sep 21
Aligning with Lived Experience: Heterogeneous Benefits of Fine Tuning in Mental Health Support Generation
The paper introduces the COmmunity-centered Peer Engaged Support (COPES) dataset and a three‑axis evaluation framework to gauge how well Large Language Models (LLMs) align with community perspectives on mental‑health support queries. Experiments show that fine‑tuning LLMs on COPES improves strategy alignment and emotion‑tone alignment by over 50% for general‑purpose models, yet these gains are uneven across subreddits and coping strategies. The study also finds that post‑training shifts the model’s recommendations toward problem‑focused advice while reducing emotion‑focused responses, indicating persistent disparities in performance across different communities and needs.
arXiv AI
Sep 23
Building Trustworthy Mental Health Benchmarks on Bluesky: A Validation-Aware Weak-Supervision Framework
arXiv:2609.22696v1 Announce Type: new Abstract: Decentralized social media platforms create new opportunities and challenges for computational mental health research because data access, moderation,...
arXiv Machine Learning
Oct 22
RubRIX: Rubric-Driven Risk Mitigation in Caregiver-AI Interactions
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...
arXiv AI
Sep 15
LLM-Based Schema-Aware Split Learning for Privacy-Preserving Mental Distress Prediction Across Heterogeneous Surveys
arXiv:2609.15871v1 Announce Type: cross Abstract: Rising societal and lifestyle complexity has been linked to a growing prevalence of mental distress worldwide. Educational institutions, workplaces,...
arXiv Computation and Language
Oct 8
Handle with CARE: Can LLMs Reproduce How Online Communities React?
arXiv:2605.27388v2 Announce Type: replace Abstract: Large language models (LLMs) are increasingly used as proxies for computational social analysis, yet faithfully representing the "thick description...
arXiv AI
Jun 9
Testing the Black Box: Structural Barriers to Independent Evaluation of Consumer-Facing Health LLMs
arXiv:2606. 08483v1 Announce Type: new Abstract: Background: Consumer-facing large language models are now a common source of health information, and they interpret and personalize responses rather than retrieve them.