# Helping the Helper: Supporting Peer Counselors via AI-Empowered Practice and Feedback

arXiv: https://arxiv.org/abs/2305.08982 · HTML: https://mlsanity.com/paper/2305.08982/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 155 references that are in the map.
- First posted on arXiv 15 May 2023.

## Builds on (most cited first, 26 of 26)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Evaluating Large Language Models Trained on Code](https://mlsanity.com/paper/2107.03374/) (2021)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding](https://mlsanity.com/paper/1804.07461/) (2018)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Extracting Training Data from Large Language Models](https://mlsanity.com/paper/2012.07805/) (2020)
- [DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generation](https://mlsanity.com/paper/1911.00536/) (2019)
- [Recipes for building an open-domain chatbot](https://mlsanity.com/paper/2004.13637/) (2020)
- [Federated Learning for Mobile Keyboard Prediction](https://mlsanity.com/paper/1811.03604/) (2018)
- [Beyond Goldfish Memory: Long-Term Open-Domain Conversation](https://mlsanity.com/paper/2107.07567/) (2021)
- [HateBERT: Retraining BERT for Abusive Language Detection in English](https://mlsanity.com/paper/2010.12472/) (2020)
- [A Neural Conversational Model](https://mlsanity.com/paper/1506.05869/) (2015)
- [The Design and Implementation of XiaoIce, an Empathetic Social Chatbot](https://mlsanity.com/paper/1812.08989/) (2018)
- [Neural Responding Machine for Short-Text Conversation](https://mlsanity.com/paper/1503.02364/) (2015)
- [Gmail Smart Compose: Real-Time Assisted Writing](https://mlsanity.com/paper/1906.00080/) (2019)
- [Towards Facilitating Empathic Conversations in Online Mental Health Support: A Reinforcement Learning Approach](https://mlsanity.com/paper/2101.07714/) (2021)
- [Human-AI Collaboration Enables More Empathic Conversations in Text-based Peer-to-Peer Mental Health Support](https://mlsanity.com/paper/2203.15144/) (2022)
- [Smart Reply: Automated Response Suggestion for Email](https://mlsanity.com/paper/1606.04870/) (2016)
- [Modeling Motivational Interviewing Strategies On An Online Peer-to-Peer Counseling Platform](https://mlsanity.com/paper/2211.05182/) (2022)
- [Human-in-the-loop Abstractive Dialogue Summarization](https://mlsanity.com/paper/2212.09750/) (2022)

## Built on by (most cited first, 23 of 23)

- [The Rise and Potential of Large Language Model Based Agents: A Survey](https://mlsanity.com/paper/2309.07864/) (2023)
- [Rehearsal: Simulating Conflict to Teach Conflict Resolution](https://mlsanity.com/paper/2309.12309/) (2023)
- [Benefits and Harms of Large Language Models in Digital Mental Health](https://mlsanity.com/paper/2311.14693/) (2023)
- [Empowering Psychotherapy with Large Language Models: Cognitive Distortion Detection through Diagnosis of Thought Prompting](https://mlsanity.com/paper/2310.07146/) (2023)
- [PATIENT-Psi: Using Large Language Models to Simulate Patients for Training Mental Health Professionals](https://mlsanity.com/paper/2405.19660/) (2024)
- [Harnessing Large Language Models' Empathetic Response Generation Capabilities for Online Mental Health Counselling Support](https://mlsanity.com/paper/2310.08017/) (2023)
- [Social Skill Training with Large Language Models](https://mlsanity.com/paper/2404.04204/) (2024)
- [Adaptive Instrument Design for Indirect Experiments](https://mlsanity.com/paper/2312.02438/) (2023)
- [Hanging in the Balance: Pivotal Moments in Crisis Counseling Conversations](https://mlsanity.com/paper/2506.03941/) (2025)
- [Found in Translation: Measuring Multilingual LLM Consistency as Simple as Translate then Evaluate](https://mlsanity.com/paper/2505.21999/) (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)
- [Interacting Large Language Model Agents. Interpretable Models and Social Learning](https://mlsanity.com/paper/2411.01271/) (2024)
- [Envisioning an AI-Enhanced Mental Health Ecosystem](https://mlsanity.com/paper/2503.14883/) (2025)
- [Therapy as an NLP Task: Comparing LLMs and Human Peers Behaviors in CBT Sessions](https://mlsanity.com/paper/2409.02244/) (2024)
- [From "Help" to Helpful: A Hierarchical Assessment of LLMs in Mental e-Health Applications](https://mlsanity.com/paper/2602.18443/) (2026)
- [CAIA in Practice: Field Evaluation of an AI-Assisted Support System for Text-Based Online Counselling](https://mlsanity.com/paper/2608.22251/) (2026)
- [When LLM Therapists Become Salespeople: Evaluating Large Language Models for Ethical Motivational Interviewing](https://mlsanity.com/paper/2503.23566/) (2025)
- [Exposure to Content Written by Large Language Models Can Reduce Stigma Around Opioid Use Disorder in Online Communities](https://mlsanity.com/paper/2504.10501/) (2025)
- ["Is This Really a Human Peer Supporter?": Misalignments Between Peer Supporters and Experts in LLM-Supported Interactions](https://mlsanity.com/paper/2506.09354/) (2025)
- [Multi-dimensional Assessment and Explainable Feedback for Counselor Responses to Client Resistance in Text-based Counseling with LLMs](https://mlsanity.com/paper/2602.21638/) (2026)
- [CounselReflect: Opportunities and Challenges for Designing Tools to Support Self-Reflection on Mental Health and Well-Being Conversations with AI](https://mlsanity.com/paper/2603.29429/) (2026)
- [MIThinker: A Plug-and-Play Policy-Optimized Thinker For Motivational Interviewing Counseling](https://mlsanity.com/paper/2606.29265/) (2026)
- [PeerPen: AI-Assisted Writing for Online Mental Health Peer Support](https://mlsanity.com/paper/2609.14886/) (2026)

## Abstract (arXiv)

Millions of users come to online peer counseling platforms to seek support. However, studies show that online peer support groups are not always as effective as expected, largely due to users' negative experiences with unhelpful counselors. Peer counselors are key to the success of online peer counseling platforms, but most often do not receive appropriate training.Hence, we introduce CARE: an AI-based tool to empower and train peer counselors through practice and feedback. Concretely, CARE helps diagnose which counseling strategies are needed in a given situation and suggests example responses to counselors during their practice sessions. Building upon the Motivational Interviewing framework, CARE utilizes large-scale counseling conversation data with text generation techniques to enable these functionalities. We demonstrate the efficacy of CARE by performing quantitative evaluations and qualitative user studies through simulated chats and semi-structured interviews, finding that CARE especially helps novice counselors in challenging situations. The code is available at https://github.com/SALT-NLP/CARE