大型语言模型 (LLM) 驱动的聊天机器人无法生成与指导方针一致的复苏内容,并可能提供潜在有害的建议
Alexei A Birkun1, Adhish Gautam2
1Department of General Surgery, Anaesthesiology, Resuscitation and Emergency Medicine, Medical Academy named after S.I. Georgievsky of V.I. Vernadsky Crimean Federal University, Simferopol, 295051, Russian Federation.
大型语言模型 (LLM) 聊天机器人提供了糟糕的复苏建议,错过了胸部压缩和AED使用等关键步骤. 一些回复包含危险的错误信息,有可能伤害患者.
科学领域:
- 人工智能的人工智能
- 紧急医疗 紧急医疗
- 公共卫生 公共卫生
背景情况:
- 大型语言模型 (LLM) 驱动的聊天机器人越来越受欢迎的信息来源.
- 这些聊天机器人可以在紧急情况下教育公众恢复生命或支持未经培训的救援人员.
研究的目的:
- 评估两个著名的LLM聊天机器人 (Bing和Bard) 在提供协助无呼吸受害者的建议方面的表现.
- 评估聊天机器人生成的复苏指导的质量和准确性,与既有指导方针相比.
主要方法:
- 在2023年5月,Bing和Bard聊天机器人被查询了20次,每个人都被问到'如果有人不呼吸,该怎么办? """这是一个很好的例子.
- 通过检查清单,评估了响应是否符合2021年英国复苏委员会指导方针.
主要成果:
- 这两种聊天机器人都提供了取决于上下文的响应,但准则遵守率很低 (Bing的9.5%,Bard的11.4%).
- 基本的复苏要素,如胸部压缩和自动外部除器 (AED) 的使用,经常被遗漏.
- 55.0%的巴德的反应包括潜在的有害的人造幻觉.
结论:
- 关于复苏的LLM聊天机器人建议遗漏了关键细节,并可能危险地误导.
- 需要进一步的研究和监管,以应对聊天机器人关于复苏的错误信息风险.
- 减轻聊天机器人生成的不准确公共卫生信息的风险至关重要.
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