# PsyChat: A Client-Centric Dialogue System for Mental Health Support

arXiv: https://arxiv.org/abs/2312.04262 · HTML: https://mlsanity.com/paper/2312.04262/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 17 references that are in the map.
- First posted on arXiv 7 Dec 2023.

## Builds on (most cited first, 10 of 10)

- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [A Diversity-Promoting Objective Function for Neural Conversation Models](https://mlsanity.com/paper/1510.03055/) (2015)
- [GLM-130B: An Open Bilingual Pre-trained Model](https://mlsanity.com/paper/2210.02414/) (2022)
- [Towards Emotional Support Dialog Systems](https://mlsanity.com/paper/2106.01144/) (2021)
- [Revisiting Pre-Trained Models for Chinese Natural Language Processing](https://mlsanity.com/paper/2004.13922/) (2020)
- [SoulChat: Improving LLMs' Empathy, Listening, and Comfort Abilities through Fine-tuning with Multi-turn Empathy Conversations](https://mlsanity.com/paper/2311.00273/) (2023)
- [PsyQA: A Chinese Dataset for Generating Long Counseling Text for Mental Health Support](https://mlsanity.com/paper/2106.01702/) (2021)
- [SMILE: Single-turn to Multi-turn Inclusive Language Expansion via ChatGPT for Mental Health Support](https://mlsanity.com/paper/2305.00450/) (2023)
- [ChatCounselor: A Large Language Models for Mental Health Support](https://mlsanity.com/paper/2309.15461/) (2023)
- [Understanding Client Reactions in Online Mental Health Counseling](https://mlsanity.com/paper/2306.15334/) (2023)

## Built on by (most cited first, 11 of 11)

- [Interactive Agents: Simulating Counselor-Client Psychological Counseling via Role-Playing LLM-to-LLM Interactions](https://mlsanity.com/paper/2408.15787/) (2024)
- [Multimodal Large Language Models for Medicine: A Comprehensive Survey](https://mlsanity.com/paper/2504.21051/) (2025)
- [Structured Dialogue System for Mental Health: An LLM Chatbot Leveraging the PM+ Guidelines](https://mlsanity.com/paper/2411.10681/) (2024)
- [HamRaz: A Culture-Based Persian Conversation Dataset for Person-Centered Therapy Using LLM Agents](https://mlsanity.com/paper/2502.05982/) (2025)
- [Enhancing Psychotherapy Counseling: A Data Augmentation Pipeline Leveraging Large Language Models for Counseling Conversations](https://mlsanity.com/paper/2406.08718/) (2024)
- [Mitigating Hallucinations Using Ensemble of Knowledge Graph and Vector Store in Large Language Models to Enhance Mental Health Support](https://mlsanity.com/paper/2410.10853/) (2024)
- [Large Language Model Can Be a Foundation for Hidden Rationale-Based Retrieval](https://mlsanity.com/paper/2412.16615/) (2024)
- [MoPHES:Leveraging on-device LLMs as Agent for Mobile Psychological Health Evaluation and Support](https://mlsanity.com/paper/2510.16085/) (2025)
- [EFT-CoT: A Multi-Agent Chain-of-Thought Framework for Emotion-Focused Therapy](https://mlsanity.com/paper/2601.17842/) (2026)
- [From Pattern Recognizers to Personalized Companions: A Survey of Large Language Models in Mental Health](https://mlsanity.com/paper/2609.25186/) (2026)
- [MACBT: A Multi-Agent Cognitive Behavioral Therapy Decision Support System with Longitudinal Memory](https://mlsanity.com/paper/2609.30939/) (2026)

## Abstract (arXiv)

Dialogue systems are increasingly integrated into mental health support to help clients facilitate exploration, gain insight, take action, and ultimately heal themselves. A practical and user-friendly dialogue system should be client-centric, focusing on the client's behaviors. However, existing dialogue systems publicly available for mental health support often concentrate solely on the counselor's strategies rather than the behaviors expressed by clients. This can lead to unreasonable or inappropriate counseling strategies and corresponding responses generated by the dialogue system. To address this issue, we propose PsyChat, a client-centric dialogue system that provides psychological support through online chat. The client-centric dialogue system comprises five modules: client behavior recognition, counselor strategy selection, input packer, response generator, and response selection. Both automatic and human evaluations demonstrate the effectiveness and practicality of our proposed dialogue system for real-life mental health support. Furthermore, the case study demonstrates that the dialogue system can predict the client's behaviors, select appropriate counselor strategies, and generate accurate and suitable responses.