快速工程一个信息聊天机器人用于心理健康教育,使用多代理方法来加强对快速指令的遵守:算法开发和验证
Per Niklas Waaler1, Musarrat Hussain1, Igor Molchanov1
1Department of Computer Science, UiT The Arctic University of Norway, Tromsø, Norway.
JMIR AI
|February 24, 2025
概括
批判性分析过器 (CAF) 改善了大型语言模型 (LLM) 聊天机器人在心理健康教育中的合规性. 该系统确保聊天机器人提供准确的精神分裂症信息,提高安全性和可靠性.
科学领域:
- 人工智能在心理健康中的作用
- 自然语言处理应用程序
- 精神分裂症患者教育
背景情况:
- 精神分裂症的认知障碍会影响患者的教育.
- 大型语言模型 (LLM) 提供可访问的心理健康信息.
- 在LLM的可控性和幻觉风险需要安全措施.
研究的目的:
- 开发和评估一个批判分析波器 (CAF) 系统.
- 确保LLM聊天机器人遵守说明和范围.
- 为精神分裂症患者提供经过验证的心理健康信息.
主要方法:
- 快速设计的GPT-4聊天机器人用于精神分裂症教育.
- 使用LLM代理来进行响应分析和改进的CAF系统.
- 敌对对话,以测试CAF防止角色偏移和幻觉的能力.
- 人类对聊天机器人响应的评分是关于完整性和对源材料的坚持.
主要成果:
- CAF激活显著改善了响应合规性.
- 81%的响应符合CAF启用,而只有8.3%的响应是禁用.
- CAF在维护聊天机器人的完整性和准确性方面表现出有效性.
结论:
- 在LLMs中的自我反思机制可以实现安全有效的心理健康平台.
- 这种方法平衡了LLM的灵活性与可靠的范围控制.
- 建议在现实的场景下进行进一步的测试,以便广泛采用.
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