mlsanityData to 7 Oct 2026

VCounselor: A Psychological Intervention Chat Agent Based on a Knowledge-Enhanced Large Language Model

arXiv 2403.13553 ↗First posted 20 Mar 2024cs.HC cs.AIcited by 1

On the map: LLM agents and tool use → Medical and scientific domain agents

Abstract (arXiv)

Conversational artificial intelligence can already independently engage in brief conversations with clients with psychological problems and provide evidence-based psychological interventions. The main objective of this study is to improve the effectiveness and credibility of the large language model in psychological intervention by creating a specialized agent, the VCounselor, to address the limitations observed in popular large language models such as ChatGPT in domain applications. We achieved this goal by proposing a new affective interaction structure and knowledge-enhancement structure. In order to evaluate VCounselor, this study compared the general large language model, the fine-tuned large language model, and VCounselor's knowledge-enhanced large language model. At the same time, the general large language model and the fine-tuned large language model will also be provided with an avatar to compare them as an agent with VCounselor. The comparison results indicated that the affective interaction structure and knowledge-enhancement structure of VCounselor significantly improved the effectiveness and credibility of the psychological intervention, and VCounselor significantly provided positive tendencies for clients' emotions. The conclusion of this study strongly supports that VConselor has a significant advantage in providing psychological support to clients by being able to analyze the patient's problems with relative accuracy and provide professional-level advice that enhances support for clients.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Placed in Medical and scientific domain agents, one of 16 directions in LLM agents and tool use. Builds on 16 of its 79 references that are in the map. First posted on arXiv 20 Mar 2024.

Builds on

16 of 79 references.

References · 16

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  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
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  10. 2023ChatDoctor: A Medical Chat Model Fine-Tuned on a Large Language Model Meta-AI (LLaMA) Using Medical Domain Knowledgecited by 324
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4 more
  1. 2023Will Affective Computing Emerge from Foundation Models and General AI? A First Evaluation on ChatGPTcited by 41
  2. 2023VITS2: Improving Quality and Efficiency of Single-Stage Text-to-Speech with Adversarial Learning and Architecture Designcited by 25
  3. 2023Prompt Sapper: A LLM-Empowered Production Tool for Building AI Chainscited by 24
  4. 2023Enhancing Psychological Counseling with Large Language Model: A Multifaceted Decision-Support System for Non-Professionalscited by 24

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Breakdowns in Conversational AI: Interactional Failures in Emotionally and Ethically Sensitive Contextscited by 0

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  2. 2025MindEval: Benchmarking Language Models on Multi-turn Mental Health Support
  3. 2025ESC-Judge: A Framework for Comparing Emotional Support Conversational Agents
  4. 2024Advancing Conversational Psychotherapy: Integrating Privacy, Dual-Memory, and Domain Expertise with Large Language Models
  5. 2026Structure Matters: Evaluating Multi-Agents Orchestration in Generative Therapeutic Chatbots
  6. 2023Understanding the Benefits and Challenges of Using Large Language Model-based Conversational Agents for Mental Well-being Support
  7. 2026How a Chatbot's Response Style Shapes a Classroom: A Multi-Agent Simulation of Students Consulting AI
  8. 2024Domain-Specific Improvement on Psychotherapy Chatbot Using Assistant