# Large language models for mental health

arXiv: https://arxiv.org/abs/2411.11880 · HTML: https://mlsanity.com/paper/2411.11880/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Nov 2024.

## Built on by (most cited first, 9 of 9)

- [Characterizing Delusional Spirals through Human-LLM Chat Logs](https://mlsanity.com/paper/2603.16567/) (2026)
- [Technological folie `a deux: Feedback Loops Between AI Chatbots and Mental Illness](https://mlsanity.com/paper/2507.19218/) (2025)
- [Harmful Traits of AI Companions](https://mlsanity.com/paper/2511.14972/) (2025)
- [Assessing Risks of Large Language Models in Mental Health Support: A Framework for Automated Clinical AI Red Teaming](https://mlsanity.com/paper/2602.19948/) (2026)
- [AI generates well-liked but templatic empathic responses](https://mlsanity.com/paper/2604.08479/) (2026)
- [Visual Affect Analysis: Predicting Emotions of Image Viewers with Vision-Language Models](https://mlsanity.com/paper/2602.00123/) (2026)
- [A Pilot Study on Curator-Guided Multilingual Art Description for Blind and Low-Vision Audiences with Small Vision-Language Models](https://mlsanity.com/paper/2605.31080/) (2026)
- [Stumbling Into AI Emotional Dependence: How Routine AI Interactions Reshape Human Connection](https://mlsanity.com/paper/2606.04150/) (2026)
- [Reducing Conversational Escalation in Large Language Model Dialogue with Nonviolent Communication Constraints](https://mlsanity.com/paper/2606.26106/) (2026)

## Abstract (arXiv)

Digital technologies have long been explored as a complement to standard procedure in mental health research and practice, ranging from the management of electronic health records to app-based interventions. The recent emergence of large language models (LLMs), both proprietary and open-source ones, represents a major new opportunity on that front. Yet there is still a divide between the community developing LLMs and the one which may benefit from them, thus hindering the beneficial translation of the technology into clinical use. This divide largely stems from the lack of a common language and understanding regarding the technology's inner workings, capabilities, and risks. Our narrative review attempts to bridge this gap by providing intuitive explanations behind the basic concepts related to contemporary LLMs.