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人类和GPT-4在协调游戏中使用概率术语的比较
Laurence T Maloney1,2, Maria F Dal Martello3,4, Vivian Fei3
1Department of Psychology, New York University, 6 Washington Place, Room 574, New York, NY, 10012, USA. ltm1@nyu.edu.
Scientific reports
|March 22, 2024
概括
像GPT-4这样的大型语言模型可以像人类一样准确地估计概率学短语的概率和模糊性. 然而,与GPT-4相比,人类概率估计是压缩的,在理解不确定性方面显示出微妙的差异.
科学领域:
- 认知科学 认知科学
- 自然语言处理自然语言处理.
- 人与计算机的交互
背景情况:
- 英语使用概率学术词汇 (例如"可能") 来表达不确定性.
- 有效的概率沟通对于基于共享知识的协调行动至关重要.
研究的目的:
- 评估人类和GPT-4估计概率和概率学短语的模两可的能力.
- 将GPT-4的性能与不同环境中的人类估计进行比较.
- 评估协调游戏对评估AI能力的有用性.
主要方法:
- 参与者估计了投资和医疗环境中的23个短语的概率和模两可.
- GPT-4 (OpenAI) 执行了相同的估计任务.
- 用于评估的协调游戏框架.
主要成果:
- GPT-4的概率估计与人类间评分器可靠性相当或超过.
- 人类估计显示压缩与GPT-4相比.
- 背景 (投资与医疗) 对人类和GPT-4的估计影响最小.
结论:
- 协调游戏提供了一种系统的方法来评估AI对概率语言的理解.
- GPT-4在解释概率学短语方面向人类展示了强大的,但不完全相同的性能.
- 人与人工智能概率估计的微妙差异需要进一步调查.
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