# Exposure to Content Written by Large Language Models Can Reduce Stigma Around Opioid Use Disorder in Online Communities

arXiv: https://arxiv.org/abs/2504.10501 · HTML: https://mlsanity.com/paper/2504.10501/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 114 references that are in the map.
- First posted on arXiv 8 Apr 2025.

## Builds on (most cited first, 13 of 13)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Co-Writing with Opinionated Language Models Affects Users' Views](https://mlsanity.com/paper/2302.00560/) (2023)
- [Sociotechnical Harms of Algorithmic Systems: Scoping a Taxonomy for Harm Reduction](https://mlsanity.com/paper/2210.05791/) (2022)
- [Learning from Few Examples: A Summary of Approaches to Few-Shot Learning](https://mlsanity.com/paper/2203.04291/) (2022)
- [Human-AI Collaboration Enables More Empathic Conversations in Text-based Peer-to-Peer Mental Health Support](https://mlsanity.com/paper/2203.15144/) (2022)
- [Empath: Understanding Topic Signals in Large-Scale Text](https://mlsanity.com/paper/1602.06979/) (2016)
- [GPTCoach: Towards LLM-Based Physical Activity Coaching](https://mlsanity.com/paper/2405.06061/) (2024)
- [ComPeer: A Generative Conversational Agent for Proactive Peer Support](https://mlsanity.com/paper/2407.18064/) (2024)
- [Helping the Helper: Supporting Peer Counselors via AI-Empowered Practice and Feedback](https://mlsanity.com/paper/2305.08982/) (2023)
- [Ten Social Dimensions of Conversations and Relationships](https://mlsanity.com/paper/2001.09954/) (2020)
- [Taxonomizing Representational Harms using Speech Act Theory](https://mlsanity.com/paper/2504.00928/) (2025)
- [Words Matter: Reducing Stigma in Online Conversations about Substance Use with Large Language Models](https://mlsanity.com/paper/2408.07873/) (2024)

## Abstract (arXiv)

Widespread stigma, both in the offline and online spaces, acts as a barrier to harm reduction efforts in the context of opioid use disorder (OUD). This stigma is prominently directed towards clinically approved medications for addiction treatment (MAT), people with the condition, and the condition itself. Given the potential of artificial intelligence based technologies in promoting health equity, and facilitating empathic conversations, this work examines whether large language models (LLMs) can help abate OUD-related stigma in online communities. To answer this, we conducted a series of pre-registered randomized controlled experiments, where participants read LLM-generated, human-written, or no responses to help seeking OUD-related content in online communities. The experiment was conducted under two setups, i.e., participants read the responses either once (N = 2,141), or repeatedly for 14 days (N = 107). We found that participants reported the least stigmatized attitudes toward MAT after consuming LLM-generated responses under both the setups. This study offers insights into strategies that can foster inclusive online discourse on OUD, e.g., based on our findings LLMs can be used as an education-based intervention to promote positive attitudes and increase people's propensity toward MAT.