关于大型语言模型在面对错误信息提示与人口统计信息提示时的可靠性研究
On the Reliability of Large Language Models to Misinformed and Demographically-Informed Prompts
AI总结：
本研究评估了LLM聊天机器人在气候变化和心理健康领域应对错误信息及人口统计信息提示时的表现，发现其虽能准确回答封闭式问题，但在隐私伦理及专业引导上仍需严格监管与改进。
摘要：
我们研究并观察了由大型语言模型（LLM）支持的聊天机器人在气候变化和心理健康领域内，应对包含错误信息的提示以及带有人口统计信息的问题时的行为和表现。通过定量和定性相结合的方法，我们评估了聊天机器人辨别陈述真实性的能力、对事实的遵循程度，以及其回复中是否存在偏见或错误信息。我们使用真/假（True/False）问题进行的定量分析表明，这些聊天机器人可以信赖，能够为这些封闭式问题提供正确答案。然而，从领域专家那里收集的定性见解表明，在隐私、伦理影响以及聊天机器人引导用户寻求专业服务的必要性方面仍存在担忧。我们得出结论，尽管这些聊天机器人具有巨大潜力，但将其部署在敏感领域需要仔细考量、伦理监督和严格改进，以确保它们作为人类专业知识的有效补充，而非自主解决方案。
英文摘要：
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.
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。
