人类和机器判断的信心加权集成,用于高级决策
Felipe Yáñez1, Xiaoliang Luo2, Omar Valerio Minero1
1Max Planck Institute for Neurobiology of Behavior - caesar, Bonn, Germany.
Patterns (New York, N.Y.)
|February 23, 2026
概括
人类可以通过合作来增强大语言模型 (LLM) 预测. 结合人类和机器的判断,即使人类单独表现较差,也提高了预测任务的整体团队准确性.
科学领域:
- 认知科学 认知科学
- 人工智能的人工智能
- 决策科学 决策科学 决策科学
背景情况:
- 大型语言模型 (LLM) 在特定的预测任务中表现出卓越的表现,这引发了关于人类判断的持续作用的问题.
- 人与机器的协作被认为是改善决策过程的潜在途径,即使与人工智能相比,个人人类的表现低于最佳水平.
- 有效的人机团队合作需要精心校准的信心水平和团队成员之间特定任务专业知识的多样性.
研究的目的:
- 研究人类在决策过程中的增值贡献,并与大型语言模型 (LLM) 一起进行研究.
- 开发和验证一种方法来整合人类和机器的判断,以提高整体团队的表现.
- 探索人类机器团队在哪些条件下可以超过个人成员的表现.
主要方法:
- 一种简化和扩展的贝叶斯式方法被调整为一个逻辑回归框架.
- 该框架整合了来自多个团队成员 (包括人类和机器) 的信心加权判断.
- 该方法的有效性在图像分类和神经科学预测任务上进行了测试.
主要成果:
- 将人类判断与一个或多个机器相结合,始终导致在测试任务中改善整体团队表现.
- 提出的贝叶斯方法有效地整合了各种判断,证明了其实际适用性.
- 与单个机器或人类单独表现相比,人机团队表现出更高的准确性.
结论:
- 人机协作,以强大的判断整合策略为指导,可以显著提高预测准确性.
- 这种方法为利用人类和人工智能的互补优势在决策中提供了一种实用的方法.
- 这些发现支持开发高效的人类-人工智能合作伙伴关系,以应对复杂的预测挑战.
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