Mentalic Net：面向心理健康支持的基于RAG的对话式AI开发及评估框架
Mentalic Net: Development of RAG-based Conversational AI and Evaluation Framework for Mental Health Support
AI总结：
本研究开发了基于RAG框架、结合提示工程与微调的心理健康支持对话AI Mentalic Net，构建多维度评估体系验证其性能达标，倡导以人在回路的负责任策略开发此类技术。
摘要：
大语言模型（LLMs）的出现解锁了无限可能，同时也带来了重大挑战。为此，我们开发了一款旨在辅助专业医疗保健的心理健康支持聊天机器人，重点关注其安全且有意义的应用。我们的方法包含严格的评估，覆盖准确性、共情能力、可信度、隐私性和偏见性多个维度。我们采用了检索增强生成（RAG）框架，整合了提示工程，并在新型数据集上对预训练模型进行了微调。最终得到的系统Mentalic Net Conversational AI的BERT Score达到0.898，其他评估指标也处于令人满意的范围内。我们倡导在开发这类变革性技术时采用人在回路的方法以及长期、负责任的策略，既要认识到它们改变生活的潜力，也要意识到若管理不当可能带来的风险。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
