SoulChat2.0: Psychological Counselor's Digital Twin Framework
SoulChat2.0 is a framework for constructing the digital twin of psychological counselors, designed to support the development of AI applications in mental health. It includes a data generation module and a modeling module, enabling the creation of personalized counseling models based on limited real-world counseling cases.
Detailed Introduction
SoulChat2.0 is a significant advancement in the field of mental health AI, offering a novel approach to building digital twins of psychological counselors. The framework leverages advanced LLMs to generate high-quality synthetic data that captures the language style and therapeutic techniques of specific counselors. This data is then used to fine-tune models, resulting in AI systems that can provide personalized and effective counseling support.
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HeartLink - Empathetic Psychological Model
HeartLink is an empathetic psychological model that uses a large language model fine-tuned on a large empathetic Q&A dataset. It can perceive users' emotions and experiences during conversations and provide empathetic responses using rich psychological knowledge, aiming to understand, comfort, and support users. The responses include emoji expressions to bridge the gap with users, offering psychological support and help during consultations.
Mental Health Data at WHO
The World Health Organization (WHO) provides a comprehensive collection of global health data, including mental health statistics. This resource offers insights into various mental health conditions and their prevalence, helping researchers and policymakers understand and address mental health challenges worldwide.
Psy-Insight: Mental Health Counseling Dataset
Psy-Insight is a bilingual, interpretable multi-turn dataset for mental health counseling dialogues. It includes 6,208 rounds of multi-turn counseling dialogues in English and 5,776 rounds in Chinese, annotated with step-by-step reasoning labels and multi-task labels. This dataset is designed to support the application of large language models in mental health and is suitable for tasks such as emotion classification and psychological treatment interpretation.