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Conference Paper
Comparative Study on the Performance of LLMbased Psychological Counseling Chatbots via Prompt Engineering Techniques
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- Authors
- Aram Lee, Sehwan Moon, Min Jhon, Ju-Wan Kim, Dae-Kwang Kim, Jeong Eun Kim, Kiwon Park, Eunkyoung Jeon
- Issue Date
- 2024-12
- Citation
- International Conference on Bioinformatics and Biomedicine (BIBM) 2024, pp.7072-7074
- Language
- English
- Type
- Conference Paper
- Abstract
- Recent advancements in large language models (LLMs) have opened new avenues in psychological counseling. This study leverages LLMs to develop chatbots capable of conducting empathetic and personalized counseling sessions by applying various prompt engineering techniques, including zero-shot, few-shot, meta-learning, Chain of Thought, and our newly developed Empathetic Meta-Chain (EMC) method. The EMC method demonstrated superior performance in empathy, response accuracy, interaction continuity, fluency, and understanding, as confirmed by expert evaluations. By integrating advanced empathetic strategies, the EMC chatbot significantly enhances its ability to support users' mental well-being through natural and engaging counseling interactions. These findings highlight the potential of LLM-based counseling chatbots to serve as effective tools in mental health support.
- KSP Keywords
- Health support, Language Model, Mental wellbeing, Meta-learning, Zero-shot, comparative study, mental health, superior performance