# LLUMI: Improving LLM Writing Assistance for Mental Health Support with Online Community Feedback arXiv: https://arxiv.org/abs/2605.30273 ยท HTML: https://mlsanity.com/paper/2605.30273/ ## Key facts - Cited by 2 papers in the map (data through 14 Oct 2026). - Placed in Medical LLM alignment, one of 10 directions in LLM alignment / RLHF / preference optimisation. - Builds on 26 of its 69 references that are in the map. - First posted on arXiv 28 May 2026. ## Placed in - LLM alignment / RLHF / preference optimisation / [Medical LLM alignment](https://mlsanity.com/alignment/medical-llm-alignment/) ## Builds on (most cited first, 26 of 26) - [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019) - [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://mlsanity.com/paper/2305.18290/) (2023) - [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013) - [A Computational Approach to Understanding Empathy Expressed in Text-Based Mental Health Support](https://mlsanity.com/paper/2009.08441/) (2020) - [PsyQA: A Chinese Dataset for Generating Long Counseling Text for Mental Health Support](https://mlsanity.com/paper/2106.01702/) (2021) - [Cognitive Reframing of Negative Thoughts through Human-Language Model Interaction](https://mlsanity.com/paper/2305.02466/) (2023) - [Towards Facilitating Empathic Conversations in Online Mental Health Support: A Reinforcement Learning Approach](https://mlsanity.com/paper/2101.07714/) (2021) - [Expressing stigma and inappropriate responses prevents LLMs from safely replacing mental health providers](https://mlsanity.com/paper/2504.18412/) (2025) - [Facilitating Self-Guided Mental Health Interventions Through Human-Language Model Interaction: A Case Study of Cognitive Restructuring](https://mlsanity.com/paper/2310.15461/) (2023) - [Modeling Empathy and Distress in Reaction to News Stories](https://mlsanity.com/paper/1808.10399/) (2018) - [SLM-Mod: Small Language Models Surpass LLMs at Content Moderation](https://mlsanity.com/paper/2410.13155/) (2024) - [Don't lose the message while paraphrasing: A study on content preserving style transfer](https://mlsanity.com/paper/2308.09055/) (2023) - [From Lived Experience to Insight: Unpacking the Psychological Risks of Using AI Conversational Agents](https://mlsanity.com/paper/2412.07951/) (2024) - [Prototypical Human-AI Collaboration Behaviors from LLM-Assisted Writing in the Wild](https://mlsanity.com/paper/2505.16023/) (2025) - [CounselBench: A Large-Scale Expert Evaluation and Adversarial Benchmarking of Large Language Models in Mental Health Question Answering](https://mlsanity.com/paper/2506.08584/) (2025) - [ComPO: Community Preferences for Language Model Personalization](https://mlsanity.com/paper/2410.16027/) (2024) - [MoMoE: Mixture of Moderation Experts Framework for AI-Assisted Online Governance](https://mlsanity.com/paper/2505.14483/) (2025) - [Mapping Caregiver Needs to AI Chatbot Design: Strengths and Gaps in Mental Health Support for Alzheimer's and Dementia Caregivers](https://mlsanity.com/paper/2506.15047/) (2025) - [The Effect of Moderation on Online Mental Health Conversations](https://mlsanity.com/paper/2005.09225/) (2020) - [Mental Health Coping Stories on Social Media: A Causal-Inference Study of Papageno Effect](https://mlsanity.com/paper/2302.09885/) (2023) - [Few-shot Dialogue Strategy Learning for Motivational Interviewing via Inductive Reasoning](https://mlsanity.com/paper/2403.15737/) (2024) - [PAIR-SAFE: A Paired-Agent Approach for Runtime Auditing and Refining AI-Mediated Mental Health Support](https://mlsanity.com/paper/2601.12754/) (2026) - [Interpersonal Theory of Suicide as a Lens to Examine Suicidal Ideation in Online Spaces](https://mlsanity.com/paper/2504.13277/) (2025) - [Detecting Early and Implicit Suicidal Ideation via Longitudinal and Information Environment Signals on Social Media](https://mlsanity.com/paper/2510.14889/) (2025) - [CALM-IT: Enhancing Synthetic Motivational Interviewing Dialogue Quality with Conversational Dynamics](https://mlsanity.com/paper/2601.10085/) (2026) - [VASTU: Language Models Struggle to Recognize Online Community Values](https://mlsanity.com/paper/2601.12491/) (2026) ## Built on by (most cited first, 2 of 2) - [TUX: Measuring Human--AI Tacit Understanding](https://mlsanity.com/paper/2605.30930/) (2026) - [PeerPen: AI-Assisted Writing for Online Mental Health Peer Support](https://mlsanity.com/paper/2609.14886/) (2026) ## Abstract (arXiv) 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.