arXiv Machine Learning
RubRIX: Rubric-Driven Risk Mitigation in Caregiver-AI Interactions
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arXiv AI
Jun 17
Towards Understanding and Measuring COGNITIVE ATROPHY in LLM Behaviour
arXiv:2606. 18129v1 Announce Type: cross Abstract: Recent incidents involving LLMs used for mental-health support reveal a critical evaluation gap: surface-level safety scores do not capture how models behave across realistic, emotionally sensitive interactions over time.
arXiv AI
Sep 10
LLUMI: Improving LLM Writing Assistance for Mental Health Support with Online Community Feedback
arXiv:2605.30273v2 Announce Type: replace-cross Abstract: Large language models (LLMs) show promise in generating supportive responses for mental health queries, but improving their usefulness, empat...
arXiv AI
Oct 15
When Pleasing the User Compromises Care: AI Sycophancy in Caregiver-Mediated Healthcare
arXiv:2610.14360v1 Announce Type: new Abstract: Large language model (LLM) alignment often assumes that the user interacting with a model is also the primary beneficiary of its behavior. Yet in many...
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.
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.
Hugging Face Trending Papers
Jun 29
CAREBench: A Child-Safety Risk Benchmark for Language Models
How can we evaluate whether frontier AI systems recognize child-safety risks before they escalate into explicit harm? Existing child safety evaluations focus on child sexual abuse material, yet many child-safety failures begin earlier: in model assistance that helps adults manipulate, impersonate, profile, or isolate minors, and in model responses that deepen children's emotional dependence on AI systems rather than redirecting them toward human support.