LLUMI: Improving LLM Writing Assistance for Mental Health Support with Online Community Feedback

2026-05-28

AI Summary

This work addresses the challenges of efficacy, empathy, and safety faced by large language models in mental health support by proposing LLUMI, a locally deployable open-source system. LLUMI leverages implicit upvote/downvote feedback from Reddit mental health communities to construct preference data—a first in this domain—and employs both supervised fine-tuning (SFT) and direct preference optimization (DPO) for alignment training. Comprehensive human evaluations across multiple dimensions, including readability, empathy, connectedness, actionability, and safety, demonstrate that LLUMI achieves language generation quality and supportive effectiveness comparable to closed-source models such as GPT. These results validate the feasibility and efficacy of privacy-preserving, community-feedback-driven open-source models in sensitive mental health applications.

Abstract

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 environments. LLUMI consists of two complementary components: a generation model (GM), which drafts supportive responses to mental health queries, and an improvement model (IM), which revises an initial human-crafted response. We leverage feedback signals from Reddit mental health communities, using community endorsement patterns such as upvotes and downvotes to construct chosen-rejected response pairs for Supervised Fine Tuning (SFT) and Direct Preference Optimization (DPO). We further align LLUMI using human evaluation across five dimensions: readability, empathy, connection, actionability, and safety. Our results show that, despite relying on smaller open-source models rather than proprietary cloud-based GPT models, LLUMI achieves comparable performance across linguistic analyses and human evaluations. These findings suggest that open-source models, when trained with community-derived preference signals, can support high-quality mental health support assistance while offering a more privacy-preserving alternative for sensitive support contexts.

Problem

• mental health support
• privacy-preserving
• large language models
• empathy
• data governance

Innovation

• community feedback
• privacy-preserving LLM
• preference optimization
• mental health support
• open-source models