基于RAG的聊天机器人助力心理健康支持，兼顾安全与效果。
Mentalic Net: Development of RAG-based Conversational AI and Evaluation Framework for Mental Health Support
- 采用RAG框架结合提示工程与微调，提升对话相关性。
- BERT Score达0.898，多维度评估指标均表现良好。
- 适合心理服务研究者及医疗AI开发者参考应用。
大型语言模型（LLMs）的兴起带来了无限可能，也伴随显著挑战。为此，我们开发了一款旨在辅助专业医疗的心理健康支持聊天机器人，强调安全且有意义的应用。方法上采用检索增强生成（RAG）框架，结合提示工程，并在新构建的数据集上微调预训练模型。系统名为Mentalic Net Conversational AI，取得BERT Score为0.898，其他评估指标均处于满意范围。我们倡导人机协同模式和长期负责任的发展策略，正视此类技术改变生命潜力的同时，也警惕其管理不当带来的风险。
原文摘要 · Abstract (English)
The emergence of large language models (LLMs) has unlocked boundless possibilities, along with significant challenges. In response, we developed a mental health support chatbot designed to augment professional healthcare, with a strong emphasis on safe and meaningful application. Our approach involved rigorous evaluation, covering accuracy, empathy, trustworthiness, privacy, and bias. We employed a retrieval-augmented generation (RAG) framework, integrated prompt engineering, and fine-tuned a pre-trained model on novel datasets. The resulting system, Mentalic Net Conversational AI, achieved a BERT Score of 0.898, with other evaluation metrics falling within satisfactory ranges. We advocate for a human-in-the-loop approach and a long-term, responsible strategy in developing such transformative technologies, recognizing both their potential to change lives and the risks they may pose if not carefully managed.
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