私人演讲:大型语言模型与儿童之间的相似之处
Zhiyu Liang1, Leon On Tay1, Simon Dennis1,2
1Melbourne School of Psychological Sciences, The University of Melbourne, Melbourne, VIC, Australia.
Frontiers in artificial intelligence
|February 16, 2026
概括
像GPT-4o这样的大型语言模型 (LLM) 可以产生与儿童相似的私人演讲,特别是在自我指导的任务中. 任务的性质,而不是主题类型,显著影响了这种语言模式.
科学领域:
- 认知科学 认知科学
- 人工智能的人工智能
- 计算语言学 计算语言学
背景情况:
- 通常在儿童中观察到的私人演讲服务于自我调节和解决问题等认知功能.
- 大型语言模型 (LLM) 在生成类似人类文本方面越来越复杂,这促使人们对它们的认知能力进行调查.
- 了解LLM生成的私人演讲可以提供对人工通用智能和计算意识的见解.
研究的目的:
- 调查非推理的LLM (GPT-4o) 产生私人演讲的能力.
- 评估LLM生成的私人演讲与人类私人演讲之间的相似性.
- 探索影响LLM私人演讲内容的因素.
主要方法:
- 一个没有推理的LLM (GPT-4o) 被放置在一个模拟的单块构建场景中,使用文本提示.
- 通过使用已建立的语义框架来对儿童的私人演讲进行分类,LLM自导演的发言被引发和分类.
- 来自LLM的私人演讲类别的分布与来自块构建研究的两个人类基准进行了比较.
主要成果:
- GPT-4o的私人演讲档案与经典基准值相似性微不足道 (r = 0.01),但与最近的基准值相似性非常强 (r = 0.93).
- 这种差异归因于任务的性质 (目标导向与自我决定的游戏),而不是主题 (法学士与儿童).
- 使用GPT-3.5-Turbo-instruct进行的一项探索性研究还揭示了连续召回任务中的偶然私人演讲.
结论:
- 任务特征,特别是自我决定和支架的程度,在LLMs中显著塑造了私人演讲内容.
- 在特定的条件下,LLM证明了对私人语音生成的能力,这反映了在特定条件下的人类模式.
- 这项研究为研究私人演讲的LLM复制及其对计算意识的影响开辟了道路.
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