大型语言模型知道人类知道的吗?
Sean Trott1, Cameron Jones1, Tyler Chang1
1Department of Cognitive Science, University of California San Diego.
Cognitive science
|July 4, 2023
概括
像GPT-3这样的大型语言模型显示出一些理解他人的信仰的能力,但人类在思维理论任务中的表现表明,单独的语言不足以充分发展.
科学领域:
- 认知科学 认知科学
- 人工智能的人工智能
- 发展心理学 发展心理学
背景情况:
- 人类拥有将信仰归因于他人的能力,这是社会认知的关键组成部分.
- 这种能力的起源受到争议,其中可能包括先天的倾向和发展经验,特别是语言接触.
研究的目的:
- 研究语言暴露在发展理解他人的心理状态的能力中的作用.
- 通过评估大型语言模型 (GPT-3) 在错误信念任务上的表现来测试语言暴露假设.
主要方法:
- 对人类参与者和GPT-3语言模型进行了虚假信念任务的语言版本.
- 该模型接触到大量的人类语言,超过了典型的人类一生暴露.
主要成果:
- 人类和GPT-3都表现出对他人的信仰的敏感性,表现明显高于机会.
- 人类参与者在错误信念任务中始终表现优于语言模型.
- 尽管GPT-3的表现暴露在广泛的语言中,但它并没有完全考虑人类在信念归因方面的能力.
结论:
- 语言接触的统计学学习似乎部分促进了思维理论的发展.
- 这些发现表明,语言获取之外的机制对于人类信念归因能力的充分发展是必要的.
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