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道德机器在大型语言模型上进行了实验.

Kazuhiro Takemoto1

  • 1Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, Iizuka, Fukuoka 820-8502, Japan.

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概括
此摘要是机器生成的。

大型语言模型 (LLM) 在自动驾驶场景中显示了与人类的道德判断对齐,但与人类偏好相比,一些模型表现出明显的偏差和更不妥协的决策.

关键词:
聊天GPT 聊天 在GPT 聊天自动驾驶自动驾驶的自动驾驶.大型语言模型.这是一个道德机器.

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科学领域:

  • 人工智能伦理学 人工智能伦理学
  • 人与计算机的交互
  • 自主系统道德自主系统道德

背景情况:

  • 大型语言模型 (LLM) 越来越多地融入到关键领域,需要了解它们的伦理决策.
  • 自动驾驶系统需要强大的道德框架来应对复杂的道德困境.

研究的目的:

  • 用道德机器框架调查着名法学士的道德判断倾向.
  • 在模拟事故场景中,将LLM道德决策与既定的人类偏好进行比较.
  • 为了确定LLM和自动驾驶应用程序的人类道德推理之间的潜在差异和相似之处.

主要方法:

  • 利用道德机器框架向各种法学士提出道德困境.
  • 收集和分析了来自GPT-3.5,GPT-4,PaLM 2和Llama 2的决策数据.
  • 将LLM响应与大量人类偏好的数据集进行比较.

主要成果:

  • 总体而言,LLM和人类在优先考虑人类生命而不是动物和拯救更多个体方面保持一致.
  • 帕尔姆2和拉玛2表现出了与人类道德偏好明显的偏差.
  • 观察到显著的定量差异,LLM可能比人类做出更绝对的判断.

结论:

  • 在自动驾驶环境中,LLM表现出与人类道德判断的结合和分歧.
  • 像PaLM 2和Llama 2这样的特定LLM需要在安全关键应用中进行进一步的伦理改进.
  • 了解这些道德框架对于负责地开发和部署自动驾驶汽车中的AI至关重要.