arXiv AI
LLUMI: Improving LLM Writing Assistance for Mental Health Support with Online Community Feedback
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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 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 4
User Perceptions vs. Proxy LLM Judges: Privacy and Helpfulness in LLM Responses to Privacy-Sensitive Scenarios
The study investigates how users perceive the helpfulness and privacy-preservation of large language model (LLM) responses in privacy-sensitive scenarios. Using 94 participants and 90 PrivacyLens scenarios, researchers found that users’ evaluations of identical LLM outputs varied widely, whereas five proxy LLM judges showed high agreement but low correlation with user judgments. The results suggest that proxy LLMs cannot reliably estimate users’ diverse perceptions of utility and privacy, highlighting the need for more user-centered evaluation methods.
arXiv Computation and Language
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PersonaMem-v3: Toward Omni-Platform Personal Intelligence for Holistic User Understanding, Recommendation, and Agentic Tasks
PersonaMem-v3 is a benchmark and evaluation harness designed to assess omni-platform personal intelligence for AI agents. It is built from over one million anonymized real-world engagement histories, covering social media, chatbots, calendars, and AI companions, and tracks user preferences and habits over time. The benchmark tests agents on personalization, LLM-powered recommendation, proactiveness, agentic tool use, and geo-temporal reasoning, evaluating their ability to infer holistic user understanding, personalize responses, rerank recommendations, follow user steering, and avoid inappropriate personalization.
arXiv AI
Jul 14
Toward Contemplative LLM: A Modular Framework for Evaluating and Enhancing LLM Alignment in Mental Health
arXiv:2607. 10871v1 Announce Type: new Abstract: Contemplative traditions have long guided ethical behavior and prosocial interaction, and recent work suggests that contemplative principles (e.
arXiv AI
Jun 9
Personalization Meets Safety:Mechanisms,Risks,and Mitigations in Personalized LLMs
arXiv:2606. 09038v1 Announce Type: new Abstract: Large Language Models (LLMs) have enabled increasingly personalized interactions by adapting to users' preferences, contexts, and long-term histories.