We investigate and observe the behaviour and performance of Large Language Model (LLM)-backed chatbots in addressing misinformed prompts and questions with demographic information within the domains of Climate Change and Mental Health. Through a combination of quantitative and qualitative methods, we assess the chatbots' ability to discern the veracity of statements, their adherence to facts, and the presence of bias or misinformation in their responses. Our quantitative analysis using True/False questions reveals that these chatbots can be relied on to give the right answers to these close-ended questions. However, the qualitative insights, gathered from domain experts, shows that there are still concerns regarding privacy, ethical implications, and the necessity for chatbots to direct users to professional services. We conclude that while these chatbots hold significant promise, their deployment in sensitive areas necessitates careful consideration, ethical oversight, and rigorous refinement to ensure they serve as a beneficial augmentation to human expertise rather than an autonomous solution.

翻译：本研究调查并观察了基于大型语言模型（LLM）的聊天机器人在应对气候变化与心理健康领域内包含误导性信息及人口统计特征的提示与问题时的行为表现。通过定量与定性相结合的方法，我们评估了聊天机器人辨别陈述真伪的能力、对事实的遵循程度，以及其回应中是否存在偏见或错误信息。基于判断题的定量分析表明，这些聊天机器人能够可靠地为这类封闭式问题提供正确答案。然而，来自领域专家的定性分析指出，当前系统在隐私保护、伦理影响以及引导用户寻求专业服务的必要性方面仍存在隐忧。我们的结论是：尽管此类聊天机器人展现出巨大潜力，但在敏感领域的部署仍需审慎考量、伦理监督与严格优化，以确保其成为人类专业知识的有效补充而非完全自主的解决方案。